Particle Swarm Optimizers (PSOs) have been applied to solve Multi-Objective Optimization Problems (MOPs) for its successful applications in solving single objective optimization problems and are named as Multi-Objective PSOs (MOPSOs). However, MOPSOs are often trapped in local optima, cost more function evaluations and suffer from the curse of dimensionality. A cooperative coevolutionary and ε-...

one of the most important applications of multi-objective optimization is adjusting parameters ofpractical engineering problems in order to produce a more desirable outcome. in this paper, the decoupled sliding mode control technique (dsmc) is employed to stabilize an inverted pendulum which is a classic example of inherently unstable systems. furthermore, a new multi-objective particle swarm o...

Abstract Multi-objective particle swarm optimization algorithms encounter significant challenges when tackling many-objective problems. This is mainly because of the imbalance between convergence and diversity that occurs increasing selection pressure. In this paper, a novel adaptive MOPSO (ANMPSO) algorithm based on R2 contribution method developed to improve performance MOPSO. First, new glob...

Optimization algorithms play a critical role in electromagnetic device designs due to the ever-increasing technological and economical competition. Although evolutionary algorithm-based methods have successfully been applied different design problems, these exhibit deficiencies when solving complex problems with multimodal discontinuous objective functions, which is quite common optimization de...

This chapter presents a Discrete Multi-objective Particle Swarm Optimization (MOPSO) algorithm that determines the optimal order of activities execution within a design project that minimizes project total iterative time and cost. Numerical Design Structure Matrix (DSM) was used to model project activities’ execution order along with their interactions providing a base for calculating the objec...