Swarm intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A multimodal particle swarm optimizer based on fitness Euclidean-distance ratio
Proceedings of the 9th annual conference on Genetic and evolutionary computation
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
A Particle Swarm Optimization Method for Multimodal Optimization Based on Electrostatic Interaction
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Particle swarm optimization with gravitational interactions for multimodal and unimodal problems
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
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Evolutionary computation is inspired by nature in order to formulate metaheuristics capable to optimize several kinds of problems. A family of algorithms has emerged based on this idea; e.g. genetic algorithms, evolutionary strategies, particle swarm optimization (PSO), ant colony optimization (ACO), etc. In this paper we show a population-based metaheuristic inspired on the gravitational forces produced by the interaction of the masses of a set of bodies. We explored the physics knowledge in order to find useful analogies to design an optimization metaheuristic. The proposed algorithm is capable to find the optima of unimodal and multimodal functions commonly used to benchmark evolutionary algorithms. We show that the proposed algorithm (Gravitational Interactions Optimization - GIO) works and outperforms PSO with niches in both cases. Our algorithm does not depend on a radius parameter and does not need to use niches to solve multimodal problems. We compare GIO with other metaheuristics with respect to the mean number of evaluations needed to find the optima.