Today, engineering structures have wide application in military equipment, industrial equipment, machines, robots, etc. How to make the engineering structures away from many damages, repairs, and other costs and how to control them in a way that their life reduction would be prevented are challenging problems for researches in recent decades. They are trying to find an appropriate control appro...

A Multi-objective problems occurs wherever optimal solution necessary to be taken in the presence of tradeoffs between more than one conflicting objectives. Usually the population’s values of MOPSO algorithm are random which leads to random search quality. Particle Swarm Optimization Based on Multi Objective Functions with Uniform Design (MOPSO-UD), is proposed to enhance the accuracy of the pa...

In multi-objective particle swarm optimization (MOPSO), a proper selection of local guides significantly influences detection of non-dominated solutions in the objective/solution space and, hence, the convergence characteristics towards the Pareto-optimal set. This paper presents an algorithm based on simple heuristics for selection of local guides in MOPSO, named as HSG-MOPSO (Heuristics-based...

Particle swarm optimization (PSO) has received increasing attention in solving multi-objective economic dispatch (ED) problems in power systems because of parallel computation, faster convergence, and easier implementation. This paper presents a detailed overview of multi-objective particle swarm optimization (MOPSO) and provides a comprehensive survey on its applications in power system econom...

Neutralization of external stimuli in dynamic systems has the major role in health, life, and function of the system. Today, dynamic systems are exposed to unpredicted factors. If the factors are not considered, it will lead to irreparable damages in energy consumption and manufacturing systems. Continuous systems such as beams, plates, shells, and panels that have many applications in differen...

A newmultiobjective particle swarm optimization (MOPSO) technique for environmental/economic dispatch (EED) problem is proposed in this paper. The proposed MOPSO technique evolves a multiobjective version of PSO by proposing redefinition of global best and local best individuals in multiobjective optimization domain. The proposedMOPSO technique has been implemented to solve the EED problemwith ...

The main objective of this study is to reduce optimal vibration suppression of Timoshenko beam under non-periodic step and impulse inputs. Cantilever beam was modeled by Timoshenko theory and finite element numerical method. Stiffness (K), mass (M), and damping (C) matrices are extracted. Then, in order to control structure vibration, piezoelectric patches were used due to simultaneous dual beh...

Multi-objective particle swarm optimization (MOPSO) is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominated Pareto front for a given problem. Several approaches have been proposed to study the convergence and performance of the algorithm, particularly ...

In this paper, we illustrate a novel optimization approach based on Multi-objective Particle Swarm Optimization (MOPSO) and Fuzzy Ant Colony Optimization (FACO). The basic idea is to combine these two techniques using the best particle of the Fuzzy Ant algorithm and integrate it as the best local Particle Swarm Optimization (PSO), to formulate a new approach called hybrid MOPSO with FACO (H-MOP...