Chaotic fitness-dependent optimizer for planning and engineering design

Hardi M Mohammed,Tarik A Rashid

doi:10.1007/s00500-021-06135-z

Abstract

Fitness Dependent Optimizer (FDO) is a recent metaheuristic algorithm that mimics the reproduction behavior of the bee swarm in finding better hives. This algorithm is similar to Particle Swarm Optimization (PSO) but it works differently. The algorithm is very powerful and has better results compared to other common metaheuristic algorithms. This paper aims at improving the performance of FDO, thus, the chaotic theory is used inside FDO to propose Chaotic FDO (CFDO). Ten chaotic maps are used in the CFDO to consider which of them are performing well to avoid local optima and finding global optima. New technic is used to conduct population in specific limitation since FDO technic has a problem to amend population. The proposed CFDO is evaluated by using 10 benchmark functions from CEC2019. Finally, the results show that the ability of CFDO is improved. Singer map has a great impact on improving CFDO while the Tent map is the worst. Results show that CFDO is superior to GA, FDO, and CSO. Both CEC2013 and CEC2005 are used to evaluate CFDO. Finally, the proposed CFDO is applied to classical engineering problems, such as pressure vessel design and the result shows that CFDO can handle the problem better than WOA, GWO, FDO, and CGWO. Besides, CFDO is applied to solve the task assignment problem and then compared to the original FDO. The results prove that CFDO has better capability to solve the problem.

Highlights

Finding an optimal solution is the aim of solving optimization problems while they have various constraints
Multimodal functions have more than one optimum value, it is a challenging matter for the Chaotic FDO (CFDO), because it has to find one global optimum and avoid others, which is called local optima
The reason behind this improvement is that these three chaotic maps can improve the exploration capability of CFDO and avoid local optima and find the best global optima among various solutions

Summary

Introduction

Finding an optimal solution is the aim of solving optimization problems while they have various constraints. Traditional algorithms, such as Hill climbing, Simulated Annealing (SA), and Random Search, and metaheuristic algorithms have been used to solve complex optimization problems [2] These algorithms have some deficiencies in terms of finding global optima due to having different constraints in real-world applications, namely, engineering design problems [3], task planning problems, and economical problems [4]. Using chaos as the operation is the second way of using chaos, and it can be used to initialize the population [4], [27] Using these techniques can improve algorithms in terms of convergence speed and avoiding local optima. A mechanism is used to check whether the new position is inside the search space or not Both of the following points are used at the same time to improve the FDO so that it: 1) avoids falling into local optima and 2) enhances the search capability of it. The conclusion and future work are presented

Objectives

Results

Conclusion