Multi-objective particle swarm optimization (MOPSO) is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominated Pareto front for a given problem. Several approaches have been proposed to study the convergence and performance of the algorithm, particularly ...

In this paper, we illustrate a novel optimization approach based on Multi-objective Particle Swarm Optimization (MOPSO) and Fuzzy Ant Colony Optimization (FACO). The basic idea is to combine these two techniques using the best particle of the Fuzzy Ant algorithm and integrate it as the best local Particle Swarm Optimization (PSO), to formulate a new approach called hybrid MOPSO with FACO (H-MOP...

We consider an optimization deployment problem of multistatic radar system (MSRS). Through the antenna placing and the transmitted power allocating, we optimally deploy the MSRS for two goals: 1) the first one is to improve the coverage ratio of surveillance region; 2) the second goal is to get a even distribution of signal energy in surveillance region. In two typical working modes of MSRS, we...

In the recent years, multi-objective particle swarm optimization (MOPSO) has become quite popular in the field of multi-objective optimization. However, due to a large amount of fitness evaluations as well as the task of archive maintaining, the running time of MOPSO for optimizing some difficult problems may be quite long. This paper proposes a parallel MOPSO based on consumer-level Graphics P...

This paper presents a particle swarm optimisation (PSO) based multi-objective feature selection approach to evolving a set of non-dominated feature subsets and achieving high classification performance. Firstly, a pure multi-objective PSO (named MOPSO-SRD) algorithm, is applied to solve feature selection problems. The results of this algorithm is then used to compare with the proposed a multi-o...

Constraint handling techniques are mainly designed for evolutionary algorithms to solve constrained multiobjective optimization problems (CMOPs). Most multiojective particle swarm optimization (MOPSO) designs adopt these existing constraint handling techniques to deal with CMOPs. In the proposed constrained MOPSO, information related to particles’ infeasibility and feasibility status is utilize...

This study compares a number of selection regimes for the choosing of global best (gbest) and personal best (pbest) for swarm members in multi-objective particle swarm optimisation (MOPSO). Two distinct gbest selection techniques are shown to exist in the literature, those that do not restrict the selection of archive members and those with ‘distance’ based gbest selection techniques. Theoretic...