An Improved Chicken Swarm Optimization Algorithm and its Application in Robot Path Planning

Ximing Liang,Dechang Kou,Long Wen

doi:10.1109/access.2020.2974498

Abstract

Chicken swarm optimization (CSO) algorithm is one of very effective intelligence optimization algorithms, which has good performance in solving global optimization problems (GOPs). However, the CSO algorithm performs relatively poorly in complex GOPs for some weaknesses, which results the iteration easily fall into a local minimum. An improved chicken swarm optimization algorithm (ICSO) is proposed and applied in robot path planning. Firstly, an improved search strategy with Levy flight characteristics is introduced in the hen’s location update formula, which helps to increase the perturbation of the proposed algorithm and the diversity of the population. Secondly, a nonlinear weight reduction strategy is added in the chicken’s position update formula, which may enhance the chicken’s self-learning ability. Finally, multiple sets of unconstrained functions are used and a robot simulation experimental environment is established to test the ICSO algorithm. The numerical results show that, comparing to particle swarm optimization (PSO) and basic chicken swarm optimization (CSO), the ICSO algorithm has better convergence accuracy and stability for unconstrained optimization, and has stronger search capability in the robot path planning.

Highlights

The swarm intelligent optimization algorithm, such as genetic algorithm (GA) [1], particle swarm optimization (PSO) [2], bat algorithm (BA) [3], artificial bee colony algorithm (ABC) [4] et al, is a stochastic optimization algorithm constructed by simulating the swarm behavior of natural organisms
Like other swarm intelligent optimization algorithms, the basic chicken swarm optimization algorithm has the disadvantages of premature convergence, whose iteration is easy to fall into a local minimum, in solving the large scale optimization problem with more complexity
The improved chicken swarm optimization (ICSO) algorithm performs significantly better than any of these two algorithms, and its descending rate of objective function value is very obvious

Summary

INTRODUCTION

The swarm intelligent optimization algorithm, such as genetic algorithm (GA) [1], particle swarm optimization (PSO) [2], bat algorithm (BA) [3], artificial bee colony algorithm (ABC) [4] et al, is a stochastic optimization algorithm constructed by simulating the swarm behavior of natural organisms. Chen et al [7] updated the hen’s position formula to improve the accuracy and effectiveness of the CSO algorithm, but their algorithm needs more running time to reach the optimal solution. Chiwen et al [8] substituted Gaussian distribution with an adaptive t-distribution in the rooster position update formula, and introduced an elite opposition learning strategy in the hen position update formula These algorithms achieved good global search ability. The better convergence accuracy and higher convergence speed of the improved chicken swarm algorithm has been verified by the numerical experiments on 8 benchmark GOPs. The robot path planning experiment results show that, compare to CSO, the proposed improved chicken swarm algorithm is more effective in improving search speed and quality of path

CHICKEN SWARM ALGORITHM

NONLINEAR STRATEGIES OF DECREASING INERTIA WEIGHT

Findings

CONCLUSION