This paper presents a new multi-objective optimization algorithm in which multi-swarm cooperative strategy is incorporated into particle swarm optimization algorithm, called multi-swarm cooperative multi-objective particle swarm optimizer (MC-MOPSO). This algorithm consists of multiple slave swarms and one master swarm. Each slave swarm is designed to optimize one objective function of the mult...

A new multi-objective particle swarm optimization algorithm is presented. The proposed multi-objective particle swarm optimization algorithm is based on a MaxiMin technique previously proposed for a multi-objective genetic algorithm. The technique is applied to optimize a benchmark function set and to the design of PID controllers regarding the objectives of set-point tracking and output distur...

This paper focuses on solving a typical multi-mode resource leveling problem, in which activity duration depends on committed resources, project deadlines and other constraints. To solve this problem, we establish a multi-objective model to minimize project duration, resource requirements and resource variance. Based on the established model, a novel Elitist Vector Evaluated Particle Swarm Opti...

Multi-objective open-shop scheduling is definitely significant in practical. However, the research focused on multi-objective open-shop scheduling was relatively scarce. This article proposed a particle swarm optimization to address open-shop scheduling problems with multiple objectives. Originally, particle swarm optimization was invented to treat continuous optimization problems. In this pape...

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...

Portfolio optimization is a multi-objective problem (MOOP) with risk and profit, or some form of the two, as competing objectives. Single-objective portfolio requires trade-off coefficient to be specified in order balance two Erwin Engelbrecht proposed set-based approach single-objective optimization, namely, particle swarm (SBPSO). SBPSO selects sub-set assets that search space for secondary t...