A Cooperative Coevolution Wingsuit Flying Search Algorithm with Spherical Evolution

Jiaru Yang,Shangce Gao,Ziqian Wang,Bo Lu,Yuki Todo,Yu Zhang

doi:10.1007/s44196-021-00030-z

Abstract

The algorithm wingsuit flying search (WFS) mimics the procedure of landing the vehicle. The outstanding feature of WFS is parameterless and of rapid convergence. However, WFS also has its shortcomings, sometimes it will inevitably be trapped into local optima, thereby yield inferior solutions owing to its relatively weak exploration ability. Spherical evolution (SE) adopts a novel spherical search pattern that takes aim at splendid search ability. Cooperative coevolution is a useful parallel structure for reconciling algorithmic performance. Considering the complementary strengths of both algorithms, we herein propose a new hybrid algorithm that is comprised of SE and WFS using cooperative coevolution. During the search for optimal solutions in WFS, we replaced the original search matrix and introduced the spherical mechanism of SE, in parallel with coevolution to enhance the competitiveness of the population. The two distinct search dynamics were combined in a parallel and coevolutionary way, thereby getting a good search performance. The resultant hybrid algorithm, CCWFSSE, was tested on the CEC2017 benchmark set and 22 CEC 2011 real-world problems. The experimental data obtained can verify that CCWFSSE outperforms other algorithms in aspects of effectiveness and robustness.

Highlights

In terms of human development, several problem-solving processes are often heuristic [1,2,3]
By studying the mechanism of metaheuristic algorithms, we find that most metaheuristic algorithms have two characteristics in common
The interaction may have a negative impact on the Considering the different characteristics of the algorithm, we propose a new hybrid algorithm CCWFSSE

Summary

Introduction

In terms of human development, several problem-solving processes are often heuristic [1,2,3]. It was only since the 1940s that heuristics were used as a scientific method for various applications [4]. The initial cornerstone in the domain of heuristics was the emergence of evolutionary algorithms, which greatly advanced the theorization and practicalization of heuristics [5, 6]. The metaheuristic algorithm is an extension of the heuristic algorithm on which the stochastic algorithm and local search are fused [12, 13]. Metaheuristic algorithm is proposed relative to the optimization algorithm [14,15,16]

Results

Discussion

Conclusion