Particle Swarm Optimization (PSO) has received increased attention in the optimization research community since its first appearance. Regarding multi-objective optimization, a considerable number of algorithms based on Multi-Objective Particle Swarm Optimizers (MOPSOs) can be found in the specialized literature. Unfortunately, no experimental comparisons have been made in order to clarify which...

This paper proposes, an efficient variant of particle swarm optimization (PSO) to solve the multi-objective optimal reactive power flow (ORPF) based flexible AC transmission system (FACTS) using multi STATCOM Controllers by adjusting dynamically their parameters setting. Two objectives function are considered (power loss and voltage deviation) to validate the robustness of the proposed approach...

Gait identification task becomes difficult due to the change of appearance by different cofactors (e.g., shoe, surface, carrying, view, and clothing). Some parts of gait are affected by cofactors and other parts remains unaffected. Most of the gait identification systems consider only most effective parts thereby omitting less effective parts. However some significant features for gait identifi...

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...

Many real world optimization problems are dynamic, meaning that their optimal solutions are time-varying. In recent years, an effective approach to address these problems has been the multi-swarm PSO (mPSO). Despite this, we believe that there is still room for improvement and, in this contribution we propose two simple strategies to increase the effectiveness of mPSO. The first one faces the d...

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...

A newmultiobjective particle swarm optimization (MOPSO) technique for environmental/economic dispatch (EED) problem is proposed in this paper. The proposed MOPSO technique evolves a multiobjective version of PSO by proposing redefinition of global best and local best individuals in multiobjective optimization domain. The proposedMOPSO technique has been implemented to solve the EED problemwith ...

In this paper, a multi-objective particle swarm optimization algorithm with a new global best (gbest) selection strategy is proposed for dealing with multi-objective problems. In multi-objective particle swarm optimization, gbest plays an important role in convergence and diversity of solutions. A K-means algorithm and proportional distribution based approach is used to select gbest from the ar...

Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track a changing optimum over time. In this paper, we present new variants of Particle Swarm Optimization (PSO) specifically designed to work well in dynamic environments. The main idea is to extend the single population PSO and Charged Particle Swarm Optimization (CPSO) methods by constructi...