The paper presents a novel paradigm of the original particle swarm concept, based on the idea of having two types of agents in the swarm; the ‘‘explorers’’ and the ‘‘settlers’’, that could dynamically exchange their role in the search process. The explorers’ task is to continuously explore the search domain, while the settlers set out to refine the search in a promising region currently found by the swarm. To obtain this particle task differentiation, the numerical coefficients of the cognitive and social component of the stochastic acceleration as well as the inertia weight were related to the distance of each particle from the best position found so far by the swarm, each of them with a proper distribution over the swarm. This particle task differentiation enhances the local search ability of the particles closer to gbest and improves the exploration ability of the particles as the distance from gbest increases. The originality of this approach is based on the particle task differentiation and on the dynamical adjustment of the particle velocities at each time step on the basis of the current distance of each particle from the best position discovered so far by the swarm. To ascertain the effectiveness of the proposed variant of the PSO algorithm, several benchmark test functions, both unimodal and multi-modal, have been considered and, thanks to its task differentiation concept and adaptive behavior feature, the algorithm has demonstrated a surprising effectiveness and accuracy in identifying the optimal solution.

Adaptive acceleration coefficients for a new search diversification strategy in particle swarm optimization algorithms

ARDIZZON, GUIDO;CAVAZZINI, GIOVANNA;PAVESI, GIORGIO
2014

Abstract

The paper presents a novel paradigm of the original particle swarm concept, based on the idea of having two types of agents in the swarm; the ‘‘explorers’’ and the ‘‘settlers’’, that could dynamically exchange their role in the search process. The explorers’ task is to continuously explore the search domain, while the settlers set out to refine the search in a promising region currently found by the swarm. To obtain this particle task differentiation, the numerical coefficients of the cognitive and social component of the stochastic acceleration as well as the inertia weight were related to the distance of each particle from the best position found so far by the swarm, each of them with a proper distribution over the swarm. This particle task differentiation enhances the local search ability of the particles closer to gbest and improves the exploration ability of the particles as the distance from gbest increases. The originality of this approach is based on the particle task differentiation and on the dynamical adjustment of the particle velocities at each time step on the basis of the current distance of each particle from the best position discovered so far by the swarm. To ascertain the effectiveness of the proposed variant of the PSO algorithm, several benchmark test functions, both unimodal and multi-modal, have been considered and, thanks to its task differentiation concept and adaptive behavior feature, the algorithm has demonstrated a surprising effectiveness and accuracy in identifying the optimal solution.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3157194
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