On evolutionary exploration and exploitation
Fundamenta Informaticae
A Self-Adaptive Particle Swarm Optimization Algorithm
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 05
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
To explore or to exploit: An entropy-driven approach for evolutionary algorithms
International Journal of Knowledge-based and Intelligent Engineering Systems
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
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We present a potential extension for particle swarm optimization (PSO) to gain better optimization quality on the basis of our agent-based approach of steering metaheuristics during runtime [1]. PSO as population-based metaheuristic is structured in epochs: in each step and for each particle, the point in the search space and the velocity of the particles are computed due to current local and global best and prior velocity. During this optimization process the PSO explores the search space only sporadically. If the swarm "finds" a local minimum the particles' velocity slows down and the probability to "escape" from this point reduces significantly. In our approach we show how to speed up the swarm to unvisited areas in the search space and explore more regions without losing the best found point and the quality of the result. We introduce a new extension of the PSO for gaining a higher quality of the found solution, which can be steered and influenced by an agent.