Recently, multi-objective particle swarm optimization (MOPSO) has shown the effectiveness
in solving multi-objective optimization problems (MOPs). However, most MOPSO algorithms …

The field of complex network clustering has been very active in the past several years. In this
paper, a discrete framework of the particle swarm optimization algorithm is proposed. Based …

The selection of swarm leaders (ie, the personal best and global best), is important in the
design of a multiobjective particle swarm optimization (MOPSO) algorithm. Such leaders are …

This paper improves a recently developed multi-objective particle swarm optimizer () that
incorporates dominance with decomposition used in the context of multi-objective …

In this paper, we propose a novel hybrid multi-objective immune algorithm with adaptive
differential evolution, named ADE-MOIA, in which the introduction of differential evolution …

The DTLZ1-DTLZ4 problems are by far one of the most commonly used test problems in the
validation and comparison of multiobjective optimization evolutionary algorithms (MOEAs) …

In this work we present two parallel PSO strategies based on multiple swarms to solve
MaOPs (Many-Objective Optimization Problems). The first strategy is based on Pareto …

The multi-objective optimization methods are traditionally based on Pareto dominance or
relaxed forms of dominance in order to achieve a representation of the Pareto front …

Network coding enables higher network throughput, more balanced traffic, and securer data
transmission. However, complicated mathematical operations incur when packets are …

The performance of multiobjective evolutionary algorithms, based on Pareto-dominance,
deteriorates when applied to many-objective problems which are the problems having …