Abstract
A new hybrid approach called Evolutionary Swarm Cooperative Algorithm (ESCA) based on the collaboration between a particle swarm optimization algorithm and an evolutionary algorithm is proposed. ESCA is designed to track moving optima in dynamic environments. ESCA uses three populations of individuals: two EA populations and one particle swarm population. The EA populations evolve by the rules of an evolutionary multimodal optimization algorithm and are used to maintain the diversity of the search. The particle swarm confers precision to the search process. Using the moving peaks benchmark the efficiency of ESCA is evaluated by means of numerical experiments.
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Lung, R.I., Dumitrescu, D. (2008). ESCA: A New Evolutionary-Swarm Cooperative Algorithm. In: Krasnogor, N., Nicosia, G., Pavone, M., Pelta, D. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2007). Studies in Computational Intelligence, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78987-1_10
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DOI: https://doi.org/10.1007/978-3-540-78987-1_10
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