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In the past two decades, multi-objective optimization has attracted increasing interests in the evolutionary computation community, and a variety of multi-objective optimization algorithms have been proposed on the basis of different population based meta-heuristics, where the family of multi-objective particle swarm optimization is among the most representative ones. While the performance of most existing multi-objective particle swarm optimization algorithms largely depends on the global or personal best particles stored in an external archive, in this paper, we propose a competitive mechanism based multi-objective particle swarm optimizer, where the particles are updated on the basis of the pairwise competitions performed in the current swarm at each generation. The performance of the proposed competitive multi-objective particle swarm optimizer is verified by benchmark comparisons with several state-of-the-art multi-objective optimizers, including three multi-objective particle swarm optimization algorithms and three multi-objective evolutionary algorithms. Experimental results demonstrate the promising performance of the proposed algorithm in terms of both optimization quality and convergence speed. [ABSTRACT FROM AUTHOR]

This paper proposes a decentralized form of quantum-inspired particle swarm optimization (QPSO) with cellular structured population (called cQPSO) for keeping the population diversity and balancing the global and local search. The cQPSO is further improved by re-designing the local attractor in the sub-population (called cQPSO -lbest ) in order to accelerate the diffusion of the best solution and thus enhance the performance of cQPSO. The particles in cQPSO and cQPSO -lbest are distributed in a two-dimensional (2D) grid and only allowed to interact with their neighbors according to the specified neighborhood, which plays a role in exploiting the search space inside the neighborhood. The overlapping particles work for delivering the information among the nearest neighborhoods acting as exploring the search space with diffusion of solutions during the evolutionary process. Theoretical studies are made to analyze the global convergence of cPSO and cQPSO- lbest based on the theory of probabilistic metric space. We systematically investigate the performance of cQPSO -lbest on 42 benchmark functions with different properties (including unimodal, multimodal, separated, shifted, rotated, noisy, and mis-scaled) and compare with a set of PSO variants with different topologies and swarm-based evolutionary algorithms (EAs). The experimental results demonstrate the better performance of cQPSO -lbest . Moreover, two real-world problems, which are two-dimensional (2D) IIR digital filter design and economic dispatch (ED) problem from power systems area, are used to evaluate cQPSO- lbest and the experimental results verified the advantages of cQPSO- lbest . [ABSTRACT FROM AUTHOR]

Abstract: In recent years, particle swarm optimization (PSO) algorithm has been used to solve global optimization problems. This algorithm is widely used as an effective optimization tool in various applications. However, traditional PSO consists of only two searching layers and thus often results in premature convergence into the local minima. Thus, multi-layer particle swarm optimization (MLPSO) is proposed in this paper to improve the performance of traditional PSO by increasing the two layers of swarms to multiple layers. The MLPSO strategy increases the diversity of searching swarms to improve its performance when solving complex problems. The experiment indicates that the novel approach improves the final results and the convergence speed. [Copyright &y& Elsevier]

Abstract: Particle swarm optimization (PSO) has been shown to yield good performance for solving various optimization problems. However, it tends to suffer from premature convergence when solving complex problems. This paper presents an enhanced PSO algorithm called GOPSO, which employs generalized opposition-based learning (GOBL) and Cauchy mutation to overcome this problem. GOBL can provide a faster convergence, and the Cauchy mutation with a long tail helps trapped particles escape from local optima. The proposed approach uses a similar scheme as opposition-based differential evolution (ODE) with opposition-based population initialization and generation jumping using GOBL. Experiments are conducted on a comprehensive set of benchmark functions, including rotated multimodal problems and shifted large-scale problems. The results show that GOPSO obtains promising performance on a majority of the test problems. [Copyright &y& Elsevier]