Numerous problems encountered in real life cannot be actually formulated as a single
objective problem; hence the requirement of Multi-Objective Optimization (MOO) had arisen …

In this article, a dynamic-neighborhood-based switching PSO (DNSPSO) algorithm is
proposed, where a new velocity updating mechanism is designed to adjust the personal …

In this paper, a novel competitive swarm optimizer (CSO) for large scale optimization is
proposed. The algorithm is fundamentally inspired by the particle swarm optimization but is …

Social learning plays an important role in behavior learning among social animals. In
contrast to individual (asocial) learning, social learning has the advantage of allowing …

Particle swarm optimizer is a well-known efficient population and control parameter-based
algorithm for global optimization of different problems. This paper focuses on a new and …

An important consideration of multiobjective optimization (MOO) is the quantitative metrics
used for defining the optimality of different solution sets, which is also the basic principle for …

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 …

This paper investigates the path planning problem for unmanned surface vehicle (USV),
wherein the goal is to find the shortest, smoothest, most economical and safest path in the …

Evolutionary multiobjective optimization has been a research area since the mid-1980s, and
has experienced a very significant activity in the last 20 years. However, and in spite of the …

Multimodal optimization amounts to finding multiple global and local optima (as opposed to
a single solution) of a function, so that the user can have a better knowledge about different …