#hill climbing algorithm

[max1461]

Hangin at the hydrothermal vent with my bros metabolizing hydrogen when suddenly I'm swept away by the current. Looks like I have to seek out energy sources now looks like I have to evolve agency. Looks like I'm going to be taking actions in order to achieve goals in the external world. Looks like I might start having preferences over world states but let's not get too economics professor on this shit. Let's not start an econ blog on this shit. I'm just saying. Just saying I might have to be an agent or whatever a powerful and indifferent hill climbing algorithm implemented in turmoil and bloodshed might transform me into an agent or something.

[pancakes-phancakes]

ok I'm climbing on the wedding hill rn,, it's not only that phil is liking the marriage tiktoks, it's the fact that the algorithm is based on what you search for even from other platforms

which could mean nothing

[chasing-stardust-22]

In other dteam video news, the latest sapnap video is at 718k at just a week post-upload (and if you remember the fight to get the shark video to 1 million you know that's a HELL of an improvement), quickly outpacing his last video as well

George's video could use more love! The algorithm is not being kind to him after the absence and that's going to make the next video have a bigger hill to climb. But I believe it can get the love it deserves!

And on the shorts end, the dnf short is now at just over half a million views!

edit: I forgot to link the video whoops

[meret118]

A new report from Popular Democracy and the Institute for Policy Studies reveals how billionaire investors have become a major driver of the nationwide housing crisis. They summarize in their own words:

Billionaire-backed private equity firms worm their way into different segments of the housing market to extract ever-increasing rents and value from multi-family rental, single-family homes, and mobile home park communities.— Global billionaires purchase billions in U.S. real estate to diversify their asset holdings, driving the creation of luxury housing that functions as “safety deposit boxes in the sky.” Estimates of hidden wealth are as high as $36 trillion globally, with billions parked in U.S. land and housing markets. — Wealthy investors are acquiring property and holding units vacant, so that in many communities the number of vacant units greatly exceeds the number of unhoused people. Nationwide there are 16 million vacant homes: that is, 28 vacant homes for every unhoused person. — Billionaire investors are buying up a large segment of the short-term rental market, preventing local residents from living in these homes, in order to cash in on tourism. These are not small owners with one unit, but corporate owners with multiple properties. — Billionaire investors and corporate landlords are targeting communities of color and low-income residents, in particular, with rent increases, high rates of eviction, and unhealthy living conditions. What’s more, billionaire-owned private equity firms are investing in subsidized housing, enjoying tax breaks and public benefits, while raising rents and evicting low-income tenants from housing they are only required to keep affordable, temporarily.

. . .

Thirty-two percent is the magic threshold, according to research funded by the real estate listing company Zillow. When neighborhoods hit rent rates in excess of 32 percent of neighborhood income, homelessness explodes. And we’re seeing it play out right in front of us in cities across America because a handful of Wall Street billionaires are making a killing.

As the Zillow study notes:

“Across the country, the rent burden already exceeds the 32 percent [of median income] threshold in 100 of the 386 markets included in this analysis��.”And wherever housing prices become more than three times annual income, homelessness stalks like the grim reaper.

That Zillow-funded study laid it out:

“This research demonstrates that the homeless population climbs faster when rent affordability — the share of income people spend on rent — crosses certain thresholds. In many areas beyond those thresholds, even modest rent increases can push thousands more Americans into homelessness.”This trend is massive.

. . .

As noted in a Wall Street Journal article titled “Meet Your New Landlord: Wall Street,” in just one suburb (Spring Hill) of Nashville:

“In all of Spring Hill, four firms … own nearly 700 houses … [which] amounts to about 5% of all the houses in town.”

This is the tiniest tip of the iceberg.

“On the first Tuesday of each month,” notes the Journal article about a similar phenomenon in Atlanta, investors “toted duffels stuffed with millions of dollars in cashier’s checks made out in various denominations so they wouldn’t have to interrupt their buying spree with trips to the bank…”

The same thing is happening in cities and suburbs all across America; agents for the billionaire investor goliaths use fine-tuned computer algorithms to sniff out houses they can turn into rental properties, making over-market and unbeatable cash bids often within minutes of a house hitting the market.

. . .

As the Bank of International Settlements summarized in a 2014 retrospective study of the years since the Reagan/Gingrich changes in banking and finance:

“We describe a Pareto frontier along which different levels of risk-taking map into different levels of welfare for the two parties, pitting Main Street against Wall Street. … We also show that financial innovation, asymmetric compensation schemes, concentration in the banking system, and bailout expectations enable or encourage greater risk-taking and allocate greater surplus to Wall Street at the expense of Main Street

.”It’s a fancy way of saying that billionaire-owned big banks and hedge funds have made trillions on housing while you and your community are becoming destitute.

. . .

Turns out it was Blackstone Group, now the world’s largest real estate investor run by a major Trump supporter. At the time they were buying $150 million worth of American houses every week, trying to spend over $10 billion. And that’s just a drop in the overall bucket.

As that new study from Popular Democracy and the Institute for Policy Studies found:

“[Billionaire Stephen Schwarzman’s] Blackstone is the largest corporate landlord in the world, with a vast and diversified real estate portfolio. It owns more than 300,000 residential units across the U.S., has $1 trillion in global assets, and nearly doubled its profits in 2021. “Blackstone owns 149,000 multi-family apartment units; 63,000 single-family homes; 70 mobile home parks with 13,000 lots through their subsidiary Treehouse Communities; and student housing, through American Campus Communities (144,300 beds in 205 properties as of 2022). Blackstone recently acquired 95,000 units of subsidized housing.”

In 2018, corporations and the billionaires that own or run them bought 1 out of every 10 homes sold in America, according to Dezember, noting that:

“Between 2006 and 2016, when the homeownership rate fell to its lowest level in fifty years, the number of renters grew by about a quarter.”

And it’s gotten worse every year since then.

. . .

Warren Buffett, KKR, and The Carlyle Group have all jumped into residential real estate, along with hundreds of smaller investment groups, and the National Home Rental Council has emerged as the industry’s premiere lobbying group, working to block rent control legislation and other efforts to control the industry.

As John Husing, the owner of Economics and Politics Inc., told The Tennessean newspaper:

“What you have are neighborhoods that are essentially unregulated apartment houses. It could be disastrous for the city.”

As Zillow found:

“The areas that are most vulnerable to rising rents, unaffordability, and poverty hold 15 percent of the U.S. population — and 47 percent of people experiencing homelessness.”

. . .

The loss of affordable homes also locks otherwise middle class families out of the traditional way wealth is accumulated — through home ownership: over 61% of all American middle-income family wealth is their home’s equity.

And as families are priced out of ownership and forced to rent, they become more vulnerable to homelessness.

Housing is one of the primary essentials of life. Nobody in America should be without it, and for society to work, housing costs must track incomes in a way that makes housing both available and affordable.

Singapore, Denmark, New Zealand, and parts of Canada have all put limits on billionaire, corporate, and foreign investment in housing, recognizing families’ residences as essential to life rather than purely a commodity. Multiple other countries are having that debate or moving to take similar actions as you read these words.

To address the housing shortage and bring down prices for renters and homeowners alike, the Harris campaign’s plan calls for a historic expansion of the Low-Income Housing Tax Credit (LIHTC) and the first-ever tax incentive for homebuilders who build starter homes sold to first-time homebuyers. Building upon the Biden-Harris administration’s proposed $20 billion innovation fund, the campaign proposes a $40 billion fund that would support local innovations in housing supply solutions, catalyze innovative methods of construction financing, and empower developers and homebuilders to design and build affordable homes.

To cut red tape and bring down housing costs, the plan calls for streamlining permitting processes and reviews, including for transit-oriented development and conversions. The agenda also proposes making certain federal lands eligible to be repurposed for affordable housing development. Collectively, these policy proposals seek to create 3 million homes in the next four years.

The campaign plan cites the Biden-Harris administration’s ongoing actions to support the lowest-income renters, including its actions to expand rental assistance for veterans and other low-income renters, increase housing supply for people experiencing homelessness, enforce fair housing laws, and hold corporate landlords accountable.

Building upon these commitments, the Harris agenda calls upon Congress to pass the “Stop Predatory Investing Act,” which would remove key tax benefits for major investors who acquire large numbers of single-family rental homes (see Memo, 7/17/23), and the “Preventing the Algorithmic Facilitation of Rental Housing Cartels Act,” which would crack down on algorithmic rent-setting software that enables price-fixing among corporate landlords.

To make homeownership attainable, Vice President Harris’s proposal would provide up to $25,000 in downpayment assistance for first-time homebuyers who have paid their rent on time for two years. First-generation homeowners – those whose parents did not own homes – would receive more generous assistance.

Vice President Harris’s economic agenda also includes proposals to lower grocery costs, lower the costs of prescription drugs and relieve medical debt, and cut taxes for workers and families with children. The plan would restore the American Rescue Plan’s expanded Child Tax Credit, which provided up to $3,600 per child for low- and middle-income families for one year before it expired in 2022, and would enact a new $6,000 tax credit for families in the first year after their child is born. These measures to reduce expenses and boost household income would also improve housing security for low-income families, who often face impossible tradeoffs between paying rent and affording food, medical care, and other basic needs.

-----

Sorry for the length, but I thought this was really important.

[mostlysignssomeportents]

Hill climbing algorithm

[yesterdays-xkcd]

Trivia: Elaine is actually her middle name.

1337: Part 2 [Explained]

Transcript

[Cueball standing and looking down at his Cueball-like friend, who is sitting on the floor near an armchair holding a cloth to his face.]

Friend: So the greatest hacker of our era is a cookie-baking mom? Cueball: Second-greatest. Friend: Oh?

[Young Elaine with a ponytail on the floor typing at a keyboard while looking at a screen connected to a computer behind it with lots of wires and open case. The computer appears to have been pieced together and there is a screwdriver lying next to her and an open box lies behind her. Little Bobby Tables is painting with a broad brush at an easel to the left. There is a clear drawing with two parts going up and one down, but it's not easy to see what it should look like. He is holding his other hand up in the air, like he is enjoying the painting.] Cueball (narrating): Mrs. Roberts had two children. Her son, Bobby, was never much for computers, but her daughter Elaine took to them like a ring in a bell.

[The front of a car is in frame with side mirror and steering wheel visible. Mrs. Roberts is waving goodbye to her daughter who is wearing a backpack and is holding a walking stick. She is about to begin climbing a staircase built into a rocky mountain side. The first 11 step are visible. Behind the two and the stair are two distant mountain peaks, and above them two clouds.] Cueball (narrating): When Elaine turned 11, her mother sent her to train under Donald Knuth in his mountain hideaway.

[Donald Knuth is standing with a pointing stick at a chalk board with graph traversal patterns on it and two blocks of unreadable text the top may be a matrix.] Cueball (narrating): For four years she studied algorithms. Donald Knuth: Child—

[Donald Knuth whips around from the board slashing the stick like a sword. Elaine jumps, making a somersault and lands on the stick balancing with her arms out.] Donald Knuth: Why is A* search wrong in this situation? Stick: swish Elaine: Memory usage! Donald Knuth: What would you use? Elaine: Dijkstra's algorithm!

[Donald Knuth and Elaine are outside, seen from behind while they are both writing on a chalkboard with a thick line down the middle to separate their work. On both sides their writing can be seen but it is unreadable. Where there is only text visible on Donald Knuth's side there is also what appears to be a drawing or matrix at the top of Elaine's. But a similar thing could be behind Donald Knuth's head. Elaine is no longer wearing her hair in a ponytail but have long straight white hair like her mom, Mrs. Roberts. To the left there is a stump from a tree, some grass and maybe a puddle of water. Further back there is a small jagged hill and a flat horizon. To the right there are four mountain peaks and a flat high plateau towards the horizon. The frame of the panel does not include the top-left and bottom-right corners, but cuts round a rectangular section of both places.] Cueball (narrating): Until one day she bested her master Donald Knuth: So our lower bound here is O(n log n) Elaine: Nope. Got it in O(n log (log n)) Cueball (narrating): And left.

[possiblyunhinged]

Sydney Sweeney’s body being labelled by chronically delusional men online as “another example of women catfishing” has me spiralling into this suffocating concern: have women just sleepwalked into even bigger body image traps while our basic rights are once again becoming battlegrounds in political campaigns?

It feels like the cherry on top after years of creative new ways capitalists have convinced us we can’t question capitalism’s chokehold on us—because affluent women have decided it’s “anti-feminist” to critique the intentions and effects of women selling us shit constantly.

Somehow, that’s the hill we’re dying on? Sincerely, choke.

Mere months ago, Sweeney was crowned the hottest woman alive with the ideal body. Now, women seem to be hurtling back toward the heroin-chic bullshit Moss and co. fronted in the ‘90s. Fabulous. Just what a healthy society needs, hey?

Social media has stealth-poisoned women’s psyches more than ever. Every influencer is selling you something. The current grift? Red LED face masks—which scientists have literally said don’t do shit compared to clinical devices. But please, spend hundreds of pounds anyway!

Filler.

Skims.

Fucking colour analysis (seriously, I wish the worst for those women, xoxo).

Gourmand scents...

WHAT THE FUCK IS EVEN GOURMAND?!

My insecurities have fed my algorithm, and now they’ve never felt heavier. Maybe it’s just me. But I doubt it. This is coming from someone who grew up when Urban Outfitters was selling shirts that said, “Nothing tastes as good as skinny feels.”

Nothing has changed, except now working-class women can go into debt—paying in instalments, no less—to break themselves in half trying to “fix” something about themselves.

I know I’m walking a thin line here—one step away from sounding like Mary Daly—but let’s be real. I’ve got 4-year-old filler in my lips. I battled an eating disorder for most of my life, so much so that at thirteen, I saved up to buy diet pills online, hid them under my pillow, and starved myself for years. I still fight the constant hum of “you’re ugly” every day.

I am the perfect customer for this hyper-consumerism we live in.

And yet... I can’t help but feel like this white, middle-class feminism is eating itself alive. The “girlboss” climb to the top has somehow convinced women not to give a shit about each other. Unless, of course, a joke is thrown their way—then, suddenly, it’s “anti-feminist.”

In the words of an actually intelligent women, rather than an emotional wrecking ball such as myself, Naomi Wolff:

"A culture fixated on female thinness is not an obsession about female beauty but an obsession about female obedience. Dieting is the most potent political sedative in women’s history."

Now, if you don't mind me, I'm going to scream into my pillow for the next few decades xoxo

[floralfractals]

2 for the end of the year mathblr ask! <3

2: What's the hardest problem you solved?

I went to a ICPC programming contest preliminary in October, and while I didn't participate myself, I did join with a coach team for shits and giggles. The hardest problem we worked on was called King of the Hill, and it went like this:

You are given an n x n grid, with n < 10000. A function has assigned a value to each point on the grid, in a way such that there are no local maxima. With no more than 10n + 100 queries, find the global maximum of the function.

I won't give out the solution for the people who are interested in trying it out themselves, but here's some pointers under the cut.

Of course, a hill climbing algorithm would work. However, since there are n^2 nodes on the grid and only 10n + 100 guesses available, this will not be efficient enough.

The grids we got were all hand-made by a jury. This means that there were some very complicated edge cases we had to work with, so you can absolutely assume the worst case scenario. That said, you might want to look at a random solution, since there are only finitely many test cases.

[theconstitutionisgayculture]

I've noticed that the people who are most against a.i. art are a)porn artists b) diversity artists who are trans/black/other minority and c) burned out has beens who haven't had a successful career. A.i art will indeed put alot of artists out of work, but as many other professional artists have said, if you have a unique style and an agent/program that helps make sure your art isn't plagiarized then your good. Of course if your a new artist and your looking to make a career then you have some very steep hills to climb. This will also make the use of art available to alot of other people, but things like style, aesthetic, themes and subjectivity are something an a.i. can't truly grasp.

It's very easy for professional artists who have already made a name for themselves to say that AI art is no big deal. If one were being cynical, one could almost say they see ore young up and coming artists being driven out of the business as job security in a field where very few find success and stay successful.

Anyway, the reason AI art is bad is because AI art can't exist without stealing from art work that was already created by human beings. It's a one stop IP violation machine, and if we allow AI programs to steal our creative work and churn out their own their own versions then we might as well kiss the idea of intellectual property and individual copyright behind. You want a world where the only original art comes from woke Disney and other megacorps that are able to buy politicians and get special protections? You want a future where everything you create can be stolen by a program, changed in any way the algorithm sees fit, and sold for pennies compared to what you were selling it for? Because I don't.

[iwriteasfotini]

The Marauders: Canon, Fanon, and the Battlegrounds Between

I know there is fierce contention between various camps within the Marauders fandom. Whether it revolves around ships, characterizations, or who knows what else, it’s out there. Everyone is fully entitled to their own opinion. And I’m personally unsure why there is so much animosity. People like what they like, and no one should be made to feel shame over the way they enjoy a character. 

There are things about the living Marauders era characters we can glean from canon. However, as the story is told through a single character’s POV, we have to take the narration with a grain of salt. Is it horribly inaccurate and unreliable, no. But is it biased? Absolutely. And thus how Harry views Sirius, Remus, and Severus during his teen years is not the end all be all to these characters. 

For James, Lily, Regulus, and anyone else who died during the first round against Voldemort, SO much is left open for interpretation. And there are many arguments to be made which have solid canon derived ground to stand on, arguments which at times even contradict each other. And I think this is what is so enticing and exciting about the Marauders. There is a lot of possibility depending on how you view a single character or a single event. It’s how so much fanfiction can exist without it all just being copies of the exact same story. And even when the plot lines follow a similar theme, people still love it because everyone throws their own little dash of pizzazz on these characters. 

How I relate to and then project the cast is heavily influenced by ME. Who I am as a person. And for you, the characterizations I portray may not be your cup of tea. Awesome! We shall continue to both enjoy the fandom gleaning what we love from the creativity of those who contribute. But it means I can’t complain when you don’t love my work, and you can’t complain when not enough people write about your ship or the characterization you connect with. We have to amiably agree to disagree, and find common ground from which to base our shallow internet understanding of the other. 

Even fanon is contested, as it should be. Maybe a majority of us agree Wolfstar is fanon, but people who ship Prongsfoot would heartily disagree. What I’d love to see is more posts about what you love about a character, rather than what you hate about the general characterization adopted by the fandom or a specific characterization. I think my mind would be opened to a wider array of ships if I could understand what it is about James and Sirius’ personalities which draws them together romantically rather than just platonically. I won’t necessarily agree about how those characters manifest to make that relationship take shape, but at least I can say “ah, I can see that.” It is far preferable over someone degrading a person’s characterization of James or Sirius. 

Even when a characterization goes rouge and totally doesn’t align with canon, who cares! That fic probably won’t get super popular as many people will be scratching their heads thinking “this isn't the [insert name] I know and love.” But if someone wanted to write it, good for them. They connected and reshaped a character to suit their own liking. No one says this is illegal. 

I feel like the broader Harry Potter fandom accepts that fanfiction does not necessarily have to be canon compliant or even canon relevant all the time. But for some reason, with the Marauders this sentiment is pushed to the recesses of our minds. People will climb the mountain of their ships and die on that hill. It could be generational (social media has changed how we interact with each other and how people interact virtually through fandoms as a result), it could be the nature of the characters themselves, it could be the ships. I don’t know what the catalyst is, but the things I read sometimes just make me shake my head. Thankfully tumblr’s algorithm keeps that stuff far from my feed. But sometimes people I follow reblog or post about this contention. 

I will never understand why someone feels the urge to go out of their way to write a terribly negative comment on someone’s work or a negative post about a ship they hate. Don’t like the tags, DON’T READ! Don’t like a ship, find and follow people who share your preferred ship. Don’t like the direction the fandom is headed? Dig, create, and stop complaining about how the ‘modern’ Marauders are nothing like how they are portrayed in canon. 

And accept that as the fandom grows, the way people interpret tags broadens. I’m sure fifteen years ago, a canon compliant tagged work looks a bit different from a current canon compliant tag. We can debate the minute details of canon and each of our individual interpretations or we can say, “you like the world of Harry Potter? So do I!” The only REAL rift should be between people who support JKR’s personal beliefs and those who do not. That shite’s real life folks. Not some fantasy world thought up by a single mom in the nineties. 

So spread the love! Not STD’s though cause that shite is also real. Practice consent and protection people. 

In unrelated notes, my tumblr timer app timed out for the day, kicked me off on my phone, and I promptly went to my computer and wrote this little rant. Also, it's election day and I've been avoiding the news/media/everything all day to wait until tomorrow to see the hopefully more stable 'results.'

Alright, enough for now. Night all!

Except I just saw some Drarry pirate AU art I'm probably going to go reblog because for some reason it really hit home. And by home I mean it was f*$%ing hot. :-)

[futureelectronic1159]

Nexperia Energy Harvesting MPPT Technology Explained

https://www.futureelectronics.com/m/nexperia. Nexperia's Energy Harvesting PMIC uses the advanced Maximum Power Point Tracking (MPPT) algorithm to harvest energy for ultra-low power IoT sensors/nodes. MPPT uses an embedded hill-climbing algorithm to deliver the maximum power to the load. https://youtu.be/yWnLrX9O7qg

[lithionpower]

Advanced Battery Management Systems in Electric Vehicles

Electric vehicles (EVs) are no longer a futuristic dream—they’re here, humming silently on our roads, promising a cleaner, greener tomorrow. At the heart of every EV lies its battery, a powerhouse that dictates range, performance, and longevity. But batteries are more than just energy storage units; they’re complex systems that need constant oversight to perform at their best. Enter the unsung hero of the EV world: the Advanced Battery Management System (BMS). As of March 4, 2025, these systems are evolving at lightning speed, revolutionizing how we drive and interact with electric mobility. Let’s dive into what makes an advanced BMS tick, why it’s critical for EVs, and where it’s headed next.

What Is a Battery Management System?

Think of a BMS as the brain behind the battery. It’s an electronic system that monitors, controls, and optimizes the performance of an EV’s battery pack—typically made up of lithium-ion cells. A basic BMS keeps tabs on voltage, current, and temperature, ensuring the battery doesn’t overcharge, overheat, or discharge too deeply. But an advanced BMS? That’s a whole different beast. It leverages cutting-edge algorithms, real-time data, and sometimes even artificial intelligence (AI) to push the limits of safety, efficiency, and lifespan.

In 2025, EVs are expected to travel farther, charge faster, and last longer than ever before, and advanced BMS technology is a big reason why. From predicting battery health to balancing individual cells, these systems are the glue holding the EV revolution together.

Why Advanced BMS Matters in EVs

Batteries are the most expensive and delicate part of an electric vehicle. Without proper management, they can degrade quickly, overheat dangerously, or fail outright—costing drivers time, money, and peace of mind. Here’s why an advanced BMS is a game-changer:

Safety First: Lithium-ion batteries are powerful but temperamental. Overheating or overcharging can lead to thermal runaway—a chain reaction that might cause fires. Advanced BMS units use precise monitoring and predictive models to catch issues before they escalate, keeping you and your EV safe.

Maximizing Range: Range anxiety is still a hurdle for EV adoption. An advanced BMS optimizes energy use by balancing the charge across all cells, ensuring every drop of power is used efficiently. Some systems even adjust performance based on driving conditions, squeezing extra miles out of every charge.

Longer Battery Life: Replacing an EV battery isn’t cheap. By preventing over-discharge, managing temperature, and fine-tuning charge cycles, a smart BMS can extend battery lifespan significantly—saving owners from hefty repair bills down the road.

Fast Charging Without the Fallout: Fast charging is a must for modern EVs, but it generates heat and stress that can age batteries prematurely. Advanced BMS designs incorporate thermal management strategies—like liquid cooling integration—to handle high-speed charging without compromising longevity.

The Tech Behind the Magic

So, what sets an advanced BMS apart from its simpler cousins? It’s all about sophistication and adaptability. Here’s a peek under the hood:

AI and Machine Learning: By analyzing real-time data—like driving patterns, temperature fluctuations, and charging habits—AI-powered BMS units can predict battery degradation and adjust operations on the fly. Imagine a system that knows your commute and tweaks energy output to save power for that steep hill you climb every day.

State-of-X (SoX) Estimation: Advanced BMS tracks critical metrics like State of Charge (SoC), State of Health (SoH), and State of Power (SoP). These aren’t just numbers—they tell the system how much juice is left, how the battery’s aging, and how much power it can deliver at any moment. New algorithms in 2025 are making these estimates more accurate than ever.

Cell Balancing Smarts: Not all battery cells age at the same rate. An advanced BMS redistributes energy between cells—either passively (dissipating excess charge) or actively (shifting it around)—to keep everything in harmony. Some systems now do this dynamically based on real-time needs, not just preset thresholds.

Thermal Management: Heat is a battery’s worst enemy. Modern BMS designs integrate with cooling systems—think liquid or phase-change materials—to maintain optimal temperatures, especially during fast charging or high-performance driving.

Cloud Connectivity: Picture this: your BMS talks to the cloud, uploading data for analysis and downloading updates to improve performance. In 2025, this is becoming standard, enabling predictive maintenance and remote diagnostics.

The Future of BMS: What’s Next?

The EV landscape is evolving fast, and BMS technology is racing to keep up. Here’s what’s on the horizon as of March 4, 2025:

Digital Twins: Imagine a virtual replica of your EV battery, running in the cloud, that simulates its behavior in real time. Digital twin technology could let manufacturers spot weaknesses and optimize BMS settings before issues arise.

Vehicle-to-Everything (V2X): BMS units are starting to play a role in V2X systems, where EVs send power back to the grid or even power homes during outages. This demands smarter energy management, and advanced BMS is stepping up.

Cybersecurity: As BMS becomes more connected, protecting it from hackers is critical. Future systems will likely include robust encryption and anomaly detection to keep your battery—and your vehicle—secure.

Solid-State Batteries: With solid-state batteries inching closer to mass adoption, BMS will need to adapt. These batteries promise higher energy density and safety, but their unique chemistry requires new management approaches—something advanced systems are already gearing up for.

Challenges to Overcome

No tech is perfect, and advanced BMS faces its share of hurdles. Complexity drives up costs, making it tricky to scale for budget-friendly EVs. Integrating AI and cloud features also raises privacy concerns—how much data should your car share? Plus, as batteries evolve, BMS designs must keep pace, requiring constant innovation.

The Road Ahead

Advanced Battery Management Systems are the backbone of the EV revolution, turning raw battery power into reliable, efficient, and safe performance. As of March 4, 2025, they’re pushing boundaries—making EVs smarter, greener, and more practical than ever. Whether it’s extending range, slashing charge times, or ensuring your battery lasts a decade, the BMS is the silent partner making it happen.

So, next time you plug in your EV or marvel at its smooth acceleration, give a nod to the advanced BMS working tirelessly behind the scenes. It’s not just keeping your battery alive—it’s powering the future of transportation. For more information battery management system for electric vehicle

[ai-news]

Author(s): Mirko Peters Originally published on Towards AI. The steepest ascent hill climbing algorithm is a potent tool for optimization, yet it faces challenges such as local maxima, evaluation function dependency, and plateaus. By overcoming thes #AI #ML #Automation

[juliebowie]

An Introduction to Local Search in Artificial Intelligence

Summary: Local search in artificial intelligence optimizes solutions by focusing on nearby solutions within a vast search space. It offers efficiency in solving complex problems, though challenges like local optima exist. Advanced techniques like Simulated Annealing enhance its performance, making local search a valuable tool in AI-driven optimization tasks.

Introduction

Artificial Intelligence (AI) revolutionizes various sectors by enabling machines to simulate human intelligence. A crucial aspect of AI is search, which involves finding optimal solutions to complex problems. Local search in artificial intelligence focuses on exploring nearby solutions to efficiently reach an optimal or near-optimal solution. 

This blog aims to introduce local search, explain its significance, and compare it with other search techniques. By understanding local search, you’ll gain insights into its practical applications and benefits in solving real-world problems.

What is Local Search in Artificial Intelligence?

In the context of Artificial Intelligence (AI), local search refers to optimization techniques that explore the solution space by iteratively improving a candidate solution based on its local neighborhood. 

Unlike global search methods that attempt to explore the entire search space, local search focuses on finding a better solution by making small, incremental changes. This approach is often used when the search space is too large to be navigated comprehensively.

Local search differs from global search techniques primarily in its scope. Global search methods, such as exhaustive search or branch-and-bound, aim to explore all possible solutions or systematically eliminate large portions of the search space.

In contrast, local search algorithms concentrate on local neighborhoods, making them more efficient in certain scenarios but potentially missing the global optimum.

Local search is particularly effective for problems with a vast search space where finding an exact solution is impractical. Examples include scheduling tasks, optimizing routes for delivery, and tuning parameters in machine learning models. These problems benefit from local search’s ability to quickly improve solutions, even if it cannot guarantee a globally optimal result.

Read: Advantages and Disadvantages of Artificial Intelligence.

Types of Local Search Algorithms

Local search algorithms play a crucial role in solving optimization problems by iteratively exploring the solution space. These algorithms focus on improving the current solution by making small changes, known as "moves," to find better solutions. Below are some of the most common types of local search algorithms used in Artificial Intelligence.

Hill Climbing

Hill Climbing is one of the simplest local search algorithms. It starts with an arbitrary solution and iteratively makes incremental changes to improve the solution's value. The algorithm continuously "climbs" towards a better solution by selecting the neighboring state with the highest value. 

However, Hill Climbing can get stuck in local optima, where no neighboring solution is better, even though the overall best solution lies elsewhere.

Simulated Annealing

Simulated Annealing mimics the process of annealing in metallurgy, where a material is heated and then slowly cooled to reduce defects. This algorithm introduces randomness to escape local optima. 

It occasionally allows worse solutions to be accepted with a probability that decreases over time, enabling the algorithm to explore a broader solution space. This approach helps Simulated Annealing find a global optimum more effectively than Hill Climbing.

Tabu Search

Tabu Search enhances the basic local search by maintaining a "tabu list" that records recently visited solutions or moves. This list prevents the algorithm from revisiting the same solutions and getting trapped in cycles. 

By forbidding or "tabuing" certain moves, Tabu Search explores new regions of the solution space, improving its chances of finding an optimal or near-optimal solution.

Genetic Algorithms

Genetic Algorithms apply the principles of natural selection to optimize solutions. The algorithm maintains a population of solutions, combining and mutating them to create new solutions. Local search techniques can be integrated into the genetic algorithm process to refine these solutions further, enhancing the overall search process.

Applications of Local Search

Local search algorithms are widely used in various real-world applications where optimization is key. These algorithms excel in finding approximate solutions to complex problems where an exhaustive search is impractical. Below, we explore some of the most common scenarios where local search methods are applied.

Optimization Problems

Local search is particularly effective in solving optimization problems, where the goal is to find the best solution from a set of possible solutions. For example, in the field of operations research, local search is used to optimize resource allocation, minimize costs, or maximize efficiency in production processes. 

The Traveling Salesman Problem (TSP) is a classic optimization problem where local search helps in finding a near-optimal route that minimizes travel distance or time.

Scheduling

In scheduling, local search algorithms are employed to efficiently allocate tasks, resources, or events over time. These algorithms can handle complex constraints and large datasets, making them ideal for industries like manufacturing, where production schedules must be optimized to meet deadlines while minimizing downtime. 

Another application is in workforce scheduling, where the aim is to assign shifts to employees in a way that balances workload, complies with labor laws, and maximizes employee satisfaction.

Routing

Routing problems, such as those found in logistics and telecommunications, are another area where local search shines. For instance, in network design, local search algorithms are used to optimize the routing of data packets through a network, ensuring minimal latency and maximal data throughput. In transportation and logistics, these algorithms help in finding the most efficient routes for delivery trucks, reducing fuel consumption and delivery times.

Challenges and Limitations

Local search algorithms, while powerful, face several challenges that can hinder their effectiveness. Understanding these limitations and implementing strategies to overcome them is crucial for optimizing their performance.

Common Issues Faced in Local Search

One of the most significant challenges in local search is the problem of local optima. Local search algorithms, such as Hill Climbing, often get trapped in local optima, where the solution is better than neighboring solutions but not the best overall. This prevents the algorithm from finding the global optimum, leading to suboptimal results. 

Another issue is scalability. As the problem size increases, local search algorithms may struggle to explore the vast search space efficiently. The computational cost can become prohibitive, especially in complex, high-dimensional problems.

Strategies to Overcome These Challenges

To address the issue of local optima, techniques such as Simulated Annealing and Tabu Search are employed. Simulated Annealing allows the algorithm to escape local optima by accepting worse solutions temporarily, while Tabu Search uses memory structures to avoid revisiting recently explored areas. 

To enhance scalability, hybrid approaches that combine local search with other optimization techniques, such as Genetic Algorithms, can be utilized. These approaches enable more efficient exploration of large search spaces, improving the overall performance of the local search algorithm.

Further Read: Big Data and Artificial Intelligence: How They Work Together?

Frequently Asked Questions

What is local search in artificial intelligence?

Local search in artificial intelligence refers to optimization techniques that focus on iteratively improving a candidate solution by exploring its local neighborhood. Unlike global search methods, local search efficiently navigates large search spaces to find optimal or near-optimal solutions in complex problems.

How does local search differ from global search in AI?

Local search differs from global search by focusing on incremental improvements within a local neighborhood, making it more efficient for large search spaces. In contrast, global search attempts to explore the entire search space, which can be computationally expensive.

What are the challenges of local search in AI?

Local search algorithms in AI often face challenges like getting trapped in local optima and scalability issues in large search spaces. Techniques like Simulated Annealing and hybrid approaches can help overcome these limitations and enhance search performance.

Conclusion

Local search in artificial intelligence is a powerful optimization technique that excels in scenarios where global search methods fall short. By focusing on local neighborhoods, it efficiently navigates vast search spaces to find near-optimal solutions. 

Despite its challenges, such as the risk of getting stuck in local optima and scalability concerns, local search remains a valuable tool in solving complex real-world problems. With the right strategies, including hybrid approaches and advanced algorithms like Simulated Annealing and Tabu Search, local search can significantly improve the efficiency and effectiveness of AI-driven solutions.