Peak and Bad-Case Performance of Swarm and Evolutionary Optimization Algorithms
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: closed (20 February 2025) | Viewed by 6440
Special Issue Editors
Interests: swarm and evolutionary computation; computational intelligence; optimization; computer networks
Special Issue Information
Dear Colleagues,
This Special Issue focuses on swarm intelligence and evolutionary computation algorithms in general. Being stochastic, these algorithms generate better or worse solutions by chance. As a rule, in scientific research, the average performance based on the arithmetic mean is reported and analyzed. In practice, these algorithms can and should be executed multiple times (possibly in parallel) and the probability of obtaining peak performance solutions then increases arbitrarily to high certainty. Due to the parallelization trends of computing elements in recent decades, this became particularly practical. On the other hand, some application scenarios might require very high probabilities of obtaining a solution of at least some minimally acceptable quality and this is where bad-case performance matters.
Experimental studies of peak or bad-case performance of algorithms that previously showed state-of-the-art average performance are welcome. Large comparisons of peak performance or bad-case performance of swarm intelligence and evolutionary computation algorithms are welcome too, and theoretical findings concerning peak performance or bad-case performance are also welcome. Finally, parameter tuning procedures for peak performance or bad-case performance are welcome as well.
Dr. Nikola Ivković
Dr. Matej Črepinšek
Guest Editors
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Keywords
- evolutionary computation
- swarm intelligence
- natural computation
- bioinspired optimization
- metaheuristics
- NP-hard
- NPO
- optimization
- stochastic algorithms
- soft computing
- computational intelligence
- peak performance
- bad-case performance
- probabilities in performance of stochastic algorithms
- confidence intervals
- multiple runs of swarm and evolutionary algorithms
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