the present paper aims to investigate the effects of modularity and the layout of subsystems and parts of a complex system on its maintainability. for this purpose, four objective functions have been considered simultaneously: i) maximizing the level of accordance between system design and optimum modularity design,ii) maximizing the level of accessibility and the maintenance space required,iii...

Many real world optimization scenarios impose certain limitations, in terms of constraints and bounds, on various factors affecting the problem. In this paper we formulate several methods for bound handling of decision variables involved in solving a multi-objective optimization problem using particle swarm optimization algorithm. We further compare the performance of these methods on different...

Abstract There are many complex multi-objective optimization problems in the real world, which difficult to solve using traditional methods. Multi-objective particle swarm is one of effective algorithms such problems. This paper proposes a with dynamic population size (D-MOPSO), helps compensate for lack convergence and diversity brought by optimization, makes full use existing resources search...

in this paper, simultaneous placement of distributed generation, capacitor bank and protective devices are utilized to improve the efficiency of the distribution network. the objectives of the problem are reduction of active and reactive power losses, improvement of voltage profile and reliability indices and increasing distribution companies’ profit. the combination of firefly algorithm, parti...

The selection of global best (Gbest) exerts a high influence on the searching performance multi-objective particle swarm optimization algorithm (MOPSO). candidates MOPSO in external archive are always estimated to select Gbest. However, most estimation methods, considered as Gbest fixed way, which is difficult adapt varying evolutionary requirements for balance between convergence and diversity...

Vector evaluated particle swarm optimization (VEPSO) is a multi-swarm variant of the traditional particle swarm optimization (PSO) algorithm applied to multi-objective problems (MOPs). Each subobjective is allocated a single sub-swarm and knowledge transfer strategies (KTSs) are used to pass information between swarms. The original VEPSO used a ring KTS, and while VEPSO has shown to be successf...