Chaos Game Optimization Algorithm with Crossover Operator for Solving Constraint Engineering Optimization Problems
- Publisher:
- Springer Nature
- Publication Type:
- Chapter
- Citation:
- Handbook of Nature-Inspired Optimization Algorithms: The State of the Art, 2022, 213, pp. 113-134
- Issue Date:
- 2022-09-04
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 978-3-031-07516-2_6.pdf | Published version | 370.55 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Metaheuristic algorithms are intelligence optimization techniques that showed satisfactory performance in dealing with real-world constrained optimization problems. One of the most recent metaheuristic methods is Chaos Game Optimization (CGO), which has a high ability in solving various types of optimization problems. CGO is a robust metaheuristic method that is developed according to some rules in chaos theory in which fractals self-similar configuration issue is employed by chaos game methodology. This chapter introduces the Crossover based Chaos Game Optimization (CrCGO) algorithm by embedding the crossover operator in the search process of the CGO algorithm. A total number of 30 benchmark constrained engineering optimization problems are used to study the capability of CrCGO in dealing with challenging optimization problems. In addition, the results of the developed algorithm is compared with a variety range of optimization methods, including six popular, and six newly developed methods from the literature. Besides, two well-known non-parametric statistical methods, Friedman and Wilcoxon signed-rank, are utilized to analyze the performance of the developed method, and results shows the better capability of the developed method in dealing with most of the selected complex optimization problems.
Please use this identifier to cite or link to this item: