A dynamic adaptive particle swarm optimization and genetic algorithm for different constrained engineering design optimization problems

Hao Zhu,Yumei Hu,Weidong Zhu

doi:10.1177/1687814018824930

Abstract

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

Highlights

A great number of optimization algorithms have been proposed to solve different engineering design optimization problems which are usually nonlinearly constrained ones
Flowchart of the DAPSO-genetic algorithm (GA) is shown in Figure 3 and it is briefly described as follows: Step 1: Set initial values of the optimization parameters including the population size M, maximum number of generations S, maximum and minimum inertia factors vmax and vmin, respectively, accelerating factors c1 and c2, maximum and minimum selection probability hmax and hmin, respectively, crossover probability pc, upper and lower limits of the position of each particle xui and xli, respectively
Note: The boldfaced data in each table mean the best one among all the results provided by different algorithms

Summary

Introduction

A great number of optimization algorithms have been proposed to solve different engineering design optimization problems which are usually nonlinearly constrained ones. Advances in Mechanical Engineering stochastic optimization algorithms, such as the particle swarm optimization (PSO) algorithm,[2] genetic algorithm (GA),[3,4,5] firefly algorithm,[6] ant colony optimization,[7] artificial bee colony (ABC),[8] mine blast algorithm (MBA),[9] simulated annealing (SA) algorithm,[10] biogeography-based optimization (BBO) algorithm[11], have been proposed to overcome these drawbacks. These stochastic optimization algorithms are usually meta-heuristic and inspired by physical and natural phenomena. The ending criterion is usually the maximum number of iterations or a sufficiently low error bound

Methods

Conclusion