Questions tagged [meta-heuristics]

For questions related to meta-heuristics (e.g. simulated annealing, tabu search, ant colony optimization algorithms, etc).

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Can machine learning algorithms automatically create formulations of optimizing algorithms?

Suppose we want to create an optimization algorithm which should be able to find an optimum value for non-convex optimization problems. Usually meta-heuristics are used for this purpose. Designing a ...
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What is the use of utilizing q-Gaussian mutation operators in evolutionary algorithms?

There are great numbers of evolutionary algorithms for optimization of engineering problems which each of them gives its own objective function value in a defined problem. Using the q-Gaussian ...
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1 answer
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Does a differential evolution algorithm mutate its population during a generation?

I'm implementing a differential evolution algorithm and when it comes to evolving a population, the page I am referencing is vague on how the new population is generated. https://en.wikipedia.org/wiki/...
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What is the difference between Stochastic Hill Climbing and Simulated Annealing?

I am reading about local search: hill climbing, and its types, and simulated annealing One of the hill climbing versions is "stochastic hill climbing", which has the following definition: ...
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What is the difference between exploitation and exploration in the context of optimization?

In the paper Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm (2015, published in Knowledge-Based Systems) The test functions are divided to three groups: unimodal, multi-...
2 votes
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How are the lower and upper bound values of the moths determined in the Moth-Flame Optimization algorithm?

I am currently implementing the Moth-Flame Optimization (MFO) Algorithm, based on the paper: Moth-Flame Optimization Algorithm: A Novel Nature-inspired Heuristic Paradigm. To calculate the values of ...
1 vote
1 answer
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What are most commons methods to measure improvement rate in a meta-heuristic?

When I run a meta-heuristics, like a Genetic Algorithm or a Simulated Annealing, I want to have a termination criterion that stops the algorithms when there is not any significant fitness improvement. ...
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Why does Simulated Annealing not take worse solution if the energy difference becomes higher?

In Simulated Annealing, a worse solution is accepted with this probability: $$p=e^{-\frac{E(y)-E(x)}{kT}}.$$ If that understanding is correct: Why is this probability function used? This means that, ...
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2 votes
1 answer
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What are advantages of using meta-heuristic algorithms on optimization problems?

What are the advantages and disadvantages of using meta-heuristic algorithms on optimization problems? Simply, why do we use meta-heuristic algorithms, like PSO, over traditional mathematical ...
5 votes
1 answer
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What is an objective function?

Local search algorithms are useful for solving pure optimization problems, in which the aim is to find the best state according to an objective function. My question is what is the objective function?
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Why can't I reproduce the experiments in the original paper that introduced the Firefly Algorithm?

I have been trying to reproduce the experiments done in the original: Firefly Algorithm for multimodal optimization (2010) by Xin-She Yang, but so far unsuccessfully. For the moment being, I'm okay if ...
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3 votes
1 answer
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How to find proper parameter settings for a given optimization algorithm?

Is there any methodology to find proper parameter settings for a given meta-heuristic algorithm, e.g. the firefly algorithm or the cuckoo search? Is this an open issue in optimization? Is extensive ...
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What are hyper-heuristics, and how are they different from meta-heuristics?

I wanted to know what the differences between hyper-heuristics and meta-heuristics are, and what their main applications are. Which problems are suited to be solved by hyper-heuristics?
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