In this paper we propose a hybrid metaheuristic based on Particle Swarm Optimization,
which we tailor on a portfolio selection problem. To motivate and apply our hybrid …

Aiming at the problems in prairie dog optimization (PDO), such as uneven population
distribution at initialization, slow convergence, imbalance between global exploration and …

Abstract The Particle Swarm Optimization (PSO) method is sensitive to convergence at a sub-
optimum solution for complex aerospace design problems. An Adaptive Mutation-Particle …

This paper presents an improved Particle Swarm Optimizer with opposition based learning
method. The key feature of this method is that opposition based learning scheme is …

In this work, a new method for creating diversity in Particle Swarm Optimization is devised.
The key feature of this method is to derive velocity update equation for each particle in …

The large-scale multi-objective optimization problem is characterized by a large decision
space. How to design an efficient optimization algorithm that can search a large decision …

The Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic
search technique encouraged from the intrinsic manner of bee swarm seeking for their food …

Particle Swarm Optimization (PSO) has shown its good search ability in many optimization
problems. But PSO easily gets trapped into local optima while dealing with complex …

Innovative materials have been steadily produced in the machining process, and it has
stimulated substantial attention among researchers to assess the ideal parameters for the …

Optimization algorithms may be applied for solving engineering design problems. In this
study, two different evolutionary algorithms are applied to solve the multi objective welded …