computer science

Social cognitive optimization

Social cognitive optimization is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Social cognitive optimization rather than just read about it. In short: Social cognitive optimization (SCO) is a population-based metaheuristic optimization algorithm which was developed in 2002. This algorithm is based on the social cognitive theory, and the key point of the ergodicity is the process of individual learning of a set of agents with their own memory and their social learning with the knowledge points in the social sharing library.

Key takeaways

Social cognitive optimization belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Social cognitive optimization to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Social cognitive optimization from memory before moving on to harder problems.

Reference excerpt

Social cognitive optimization (SCO) is a population-based metaheuristic optimization algorithm which was developed in 2002. This algorithm is based on the social cognitive theory, and the key point of the ergodicity is the process of individual learning of a set of agents with their own memory and their social learning with the knowledge points in the social sharing library. It has been used for solving continuous optimization, integer programming, and combinatorial optimization problems. It has been incorporated into the NLPSolver extension of Calc in Apache OpenOffice.

Algorithm Let f ( x ) {\displaystyle f(x)} be a global optimization problem, where x {\displaystyle x} is a state in the problem space S {\displaystyle S} . In SCO, each state is called a knowledge point, and the function f {\displaystyle f} is the goodness function. In SCO, there are a population of N c {\displaystyle N_{c}} cognitive agents solving in parallel, with a social sharing library. Each agent holds a private memory containing one knowledge point, and the social sharing library contains a set of N L {\displaystyle N_{L}} knowledge points. The algorithm runs in T iterative learning cycles. By running as a Markov chain process, the system behavior in the tth cycle only depends on the system status in the (t − 1)th cycle. The process flow is in follows:

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Social cognitive optimization

Start with the simplest possible case. Write down what Social cognitive optimization claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Social cognitive optimization before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Social cognitive optimization ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Social cognitive optimization

In research: Social cognitive optimization appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Social cognitive optimization in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Social cognitive optimization is common in secondary-school and first-year university syllabi. It links to neighbouring topics Collective intelligence, Heuristic algorithms, Optimization algorithms and methods, so understanding it makes those chapters shorter.
In everyday life: Look for Social cognitive optimization outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Social cognitive optimization in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Social cognitive optimization means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Social cognitive optimization out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Social cognitive optimization in simple terms?

Why does Social cognitive optimization matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Social cognitive optimization?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Social cognitive optimization.