Loser-Out Tournament-Based Fireworks Algorithm for Multimodal Function Optimization

Real-world optimization problems are usually multimodal which require optimization algorithms to keep a balance between exploration and exploitation. Therefore, multimodal optimization is one of the main opportunities as well as one of the main challenges for evolutionary algorithms. In this paper,...

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Vydáno v:IEEE transactions on evolutionary computation Ročník 22; číslo 5; s. 679 - 691
Hlavní autoři: Li, Junzhi, Tan, Ying
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.10.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Competition
Evolutionary algorithm
Evolutionary algorithms
Evolutionary computation
Explosions
Fireworks
fireworks algorithm (FWA)
Fitness
Genetic algorithms
Heuristic algorithms
loser-out tournament (LoT)
multimodal optimization
Optimization
Optimization algorithms
Sociology
Sparks
Statistics
swarm intelligence
ISSN:1089-778X, 1941-0026
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Shrnutí:Real-world optimization problems are usually multimodal which require optimization algorithms to keep a balance between exploration and exploitation. Therefore, multimodal optimization is one of the main opportunities as well as one of the main challenges for evolutionary algorithms. In this paper, a loser-out tournament-based fireworks algorithm (LoTFWA) is proposed for solving multimodal optimization problems. The search manner of the conventional fireworks algorithm (FWA) is based on the cooperation of several fireworks. While in the LoTFWA, we propose competition as a new manner of interaction, in which the fireworks are compared with each other not only according to their current status but also according to their progress rate. If the fitness of a certain firework cannot catch up with the best one with its current progress rate, it is considered a loser in the competition. The losers will be eliminated and reinitialized because it is vain to continue their search processes. Reinitializing these fireworks would greatly reduce the probability of being trapped in local minima for the algorithm. Experimental results show that the proposed algorithm is very powerful in optimizing multimodal functions. It not only outperforms previous versions of the FWA, but also outperforms several famous evolutionary algorithms.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2017.2787042