An incremental ant colony algorithm with local search for continuous optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Accelerating evolution via egalitarian social learning
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Multiagent learning through neuroevolution
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
EA'11 Proceedings of the 10th international conference on Artificial Evolution
Compact Particle Swarm Optimization
Information Sciences: an International Journal
Particle swarm optimization with increasing topology connectivity
Engineering Applications of Artificial Intelligence
Bio-inspired optimisation for economic load dispatch: a review
International Journal of Bio-Inspired Computation
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Incremental social learning (ISL) was proposed as a way to improve the scalability of systems composed of multiple learning agents. In this paper, we show that ISL can be very useful to improve the performance of population-based optimization algorithms. Our study focuses on two particle swarm optimization (PSO) algorithms: a) the incremental particle swarm optimizer (IPSO), which is a PSO algorithm with a growing population size in which the initial position of new particles is biased toward the best-so-far solution, and b) the incremental particle swarm optimizer with local search (IPSOLS), in which solutions are further improved through a local search procedure. We first derive analytically the probability density function induced by the proposed initialization rule applied to new particles. Then, we compare the performance of IPSO and IPSOLS on a set of benchmark functions with that of other PSO algorithms (with and without local search) and a random restart local search algorithm. Finally, we measure the benefits of using incremental social learning on PSO algorithms by running IPSO and IPSOLS on problems with different fitness distance correlations.