Chaotic Social Group Optimization for Structural Engineering Design Problems

Naik, Anima

doi:10.1007/s42235-023-00340-2

Cited by 5 publications

(2 citation statements)

References 65 publications

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“…Recently, out−of−the−box hybrid models realize the concept of chaos by adapting them to metaheuristic algorithms. Some examples are listed in [36][37][38]. A generalized version of these classifications is presented in Figure 1.…”

Section: Related Workmentioning

confidence: 99%

Chaotic Sand Cat Swarm Optimization

et al. 2023

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In this study, a new hybrid metaheuristic algorithm named Chaotic Sand Cat Swarm Optimization (CSCSO) is proposed for constrained and complex optimization problems. This algorithm combines the features of the recently introduced SCSO with the concept of chaos. The basic aim of the proposed algorithm is to integrate the chaos feature of non-recurring locations into SCSO’s core search process to improve global search performance and convergence behavior. Thus, randomness in SCSO can be replaced by a chaotic map due to similar randomness features with better statistical and dynamic properties. In addition to these advantages, low search consistency, local optimum trap, inefficiency search, and low population diversity issues are also provided. In the proposed CSCSO, several chaotic maps are implemented for more efficient behavior in the exploration and exploitation phases. Experiments are conducted on a wide variety of well-known test functions to increase the reliability of the results, as well as real-world problems. In this study, the proposed algorithm was applied to a total of 39 functions and multidisciplinary problems. It found 76.3% better responses compared to a best-developed SCSO variant and other chaotic-based metaheuristics tested. This extensive experiment indicates that the CSCSO algorithm excels in providing acceptable results.

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Section: Related Workmentioning

confidence: 99%

Chaotic Sand Cat Swarm Optimization

et al. 2023

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“…This improved algorithm can expand the diversity of the population and search space. Naik et al [50] introduced chaos theory into the modification of the social group optimization (SGO) algorithm, by replacing constant values with chaotic maps. The chaotic social group optimization algorithm proposed by them improves its convergence speed and results in precision.…”

Section: Related Workmentioning

confidence: 99%

CTOA: Toward a Chaotic-Based Tumbleweed Optimization Algorithm

Shao

Pan

2023

Mathematics

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Metaheuristic algorithms are an important area of research in artificial intelligence. The tumbleweed optimization algorithm (TOA) is the newest metaheuristic optimization algorithm that mimics the growth and reproduction of tumbleweeds. In practice, chaotic maps have proven to be an improved method of optimization algorithms, allowing the algorithm to jump out of the local optimum, maintain population diversity, and improve global search ability. This paper presents a chaotic-based tumbleweed optimization algorithm (CTOA) that incorporates chaotic maps into the optimization process of the TOA. By using 12 common chaotic maps, the proposed CTOA aims to improve population diversity and global exploration and to prevent the algorithm from falling into local optima. The performance of CTOA is tested using 28 benchmark functions from CEC2013, and the results show that the circle map is the most effective in improving the accuracy and convergence speed of CTOA, especially in 50D.

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