Improved Salp Swarm Algorithm Based on Levy Flight and Sine Cosine Operator

J Zhang,J S Wang

doi:10.1109/access.2020.2997783

Abstract

The salp swarm algorithm (SSA) is a swarm intelligence optimization algorithm that simulates the chain movement behavior of salp populations in the sea. Aiming at the shortcomings of the SSA, such as low precision, low optimization dimension and slow convergence speed, an improved salp swarm algorithm based on Levy flight and sine cosine operator (LSC-SSA) was proposed. The Levy flight mechanism uses the route of short walks combined with long jumps to search the solution space, which can effectively improve the global exploration capability of the algorithm. Improved sine cosine operator use sine search for global exploration and cosine search for local exploitation. At the same time, an adaptively switching between the two function search methods can achieve a smooth transition between global exploration and local exploitation. In the simulation experiment, salp swarm algorithm (SSA), whale optimization algorithm (WOA), particle swarm algorithm (PSO), sine cosine algorithm (SCA), firefly algorithm (FA) and LSC-SSA were adopted for solving function optimization problems. Then, the feasibility of the improved algorithm for solving high-dimensional large-scale optimization problems and the effectiveness of the improvement strategy are evaluated. Finally, LSC-SSA was applied to train muti-layer perceptron neural network. Simulation results show that the introduction of Levy flight and improved sine cosine operator in LSC-SSA significantly improves optimization accuracy and convergence speed compared with other swarm optimization algorithms. In addition, the improved algorithm can effectively solve high-dimensional large-scale optimization problems. In the application of training muti-layer perceptron NN, the improved algorithm can avoid falling into the local optimal value and obtain the ideal classification accuracy.

Highlights

The meta-heuristic algorithm has attracted researchers’ attention due to its advantages such as simplicity, few parameters, derivation-free mechanism, and avoidance of local optimization
The LSCSSA proposed in this paper first introduced the Levy flight mechanism with a step size control factor
LSC-salp swarm algorithm (SSA) uses an improved sine cosine operator to update the position of leader, and uses sine search for global exploration and cosine search for local exploitation

Summary

INTRODUCTION

The meta-heuristic algorithm has attracted researchers’ attention due to its advantages such as simplicity, few parameters, derivation-free mechanism, and avoidance of local optimization. S. Wang: Improved SSA Based on Levy Flight and Sine Cosine Operator gradient-based optimization method needs to calculate the gradient information of the searching space, but the metaheuristic algorithm generates the solution from a random solution. The random optimization of the meta-heuristic algorithms make the searching agents widely distributed in the searching space, which will reduce the probability of falling into the local optimum. This paper uses two improvement strategies to make up for the shortcomings of SSA, and the improved SSA based on Levy flight and sine cosine operator (LSC-SSA) was proposed. The innovation of this paper is to introduce the Levy flight mechanism with step size control factor into the salp swarm algorithm, which improves the traversal and global exploration ability of the algorithm.

RELATED WORK OF SSA

IMPROVED SALP SWARM ALGORITHM BASED ON LEVY FLIGHT AND SINE COSINE OPERATOR

SINE COSINE ALGORITHM

Findings

CONCLUSION