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. 2024 Aug 29;14(1):20099.
doi: 10.1038/s41598-024-70497-1.

Bobcat Optimization Algorithm: an effective bio-inspired metaheuristic algorithm for solving supply chain optimization problems

Zoubida Benmamoun  1 , Khaoula Khlie  2 , Gulnara Bektemyssova  3 , Mohammad Dehghani  4 , Youness Gherabi  5
Affiliations

Bobcat Optimization Algorithm: an effective bio-inspired metaheuristic algorithm for solving supply chain optimization problems

Zoubida Benmamoun et al. Sci Rep. .

Abstract

Supply chain efficiency is a major challenge in today's business environment, where efficient resource allocation and coordination of activities are essential for competitive advantage. Traditional efficiency strategies often struggle for resources for the complex and dynamic network. In response, bio-inspired metaheuristic algorithms have emerged as powerful tools to solve these optimization problems. Referring to the random search nature of metaheuristic algorithms and emphasizing that no metaheuristic algorithm is the best optimizer for all optimization applications, the No Free Lunch (NFL) theorem encourages researchers to design newer algorithms to be able to provide more effective solutions to optimization problems. Motivated by the NFL theorem, the innovation and novelty of this paper is in designing a new meta-heuristic algorithm called Bobcat Optimization Algorithm (BOA) that imitates the natural behavior of bobcats in the wild. The basic inspiration of BOA is derived from the hunting strategy of bobcats during the attack towards the prey and the chase process between them. The theory of BOA is stated and then mathematically modeled in two phases (i) exploration based on the simulation of the bobcat's position change while moving towards the prey and (ii) exploitation based on simulating the bobcat's position change during the chase process to catch the prey. The performance of BOA is evaluated in optimization to handle the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100, as well as to address CEC 2020. The optimization results show that BOA has a high ability in exploration, exploitation, and balance them during the search process in order to achieve a suitable solution for optimization problems. The results obtained from BOA are compared with the performance of twelve well-known metaheuristic algorithms. The findings show that BOA has been successful in handling the CEC 2017 test suite in 89.65, 79.31, 93.10, and 89.65% of the functions for the problem dimension equal to 10, 30, 50, and 100, respectively. Also, the findings show that in order to handle the CEC 2020 test suite, BOA has been successful in 100% of the functions of this test suite. The statistical analysis confirms that BOA has a significant statistical superiority in the competition with the compared algorithms. Also, in order to analyze the efficiency of BOA in dealing with real world applications, twenty-two constrained optimization problems from CEC 2011 test suite and four engineering design problems have been selected. The findings show that BOA has been successful in 90.90% of CEC2011 test suite optimization problems and in 100% of engineering design problems. In addition, the efficiency of BOA to handle SCM applications has been challenged to solve ten case studies in the field of sustainable lot size optimization. The findings show that BOA has successfully provided superior performance in 100% of the case studies compared to competitor algorithms.

Keywords: Bio-inspired; Bobcat; Exploitation; Exploration; Metaheuristic; Optimization; Supply chain.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Photo of a Bobcat; downloaded from free media Wikimedia Commons.
Algorithm 1
Algorithm 1
Pseudo-code of BOA.
Figure 2
Figure 2
Flowchart of BOA.
Figure 3
Figure 3
Exploration and exploitation of the BOA.
Figure 4
Figure 4
Qualitative analysis of BOA.
Figure 4
Figure 4
Qualitative analysis of BOA.
Figure 5
Figure 5
Convergence curves of algorithms performances on CEC 2017 test suite (dimension = 10).
Figure 5
Figure 5
Convergence curves of algorithms performances on CEC 2017 test suite (dimension = 10).
Figure 6
Figure 6
Convergence curves of algorithms performances on CEC 2017 test suite (dimension = 30).
Figure 6
Figure 6
Convergence curves of algorithms performances on CEC 2017 test suite (dimension = 30).
Figure 7
Figure 7
Convergence curves of algorithms performances on CEC 2017 test suite (dimension = 50).
Figure 7
Figure 7
Convergence curves of algorithms performances on CEC 2017 test suite (dimension = 50).
Figure 8
Figure 8
Convergence curves of algorithms performances on CEC 2017 test suite (dimension = 100).
Figure 8
Figure 8
Convergence curves of algorithms performances on CEC 2017 test suite (dimension = 100).
Figure 9
Figure 9
Boxplot diagrams of BOA and competitor algorithms performances on CEC 2020 test suite.
Figure 10
Figure 10
Convergence curves of BOA and competitor algorithms performances on CEC 2011 test suite.
Figure 11
Figure 11
Schematics of the pressure vessel design.
Figure 12
Figure 12
Convergence curves of BOA on the pressure vessel design.
Figure 13
Figure 13
Schematics of the speed reducer design,.
Figure 14
Figure 14
Convergence curves of BOA on the speed reducer design.
Figure 15
Figure 15
Schematics of the welded beam design.
Figure 16
Figure 16
Convergence curves of BOA on the welded beam design.
Figure 17
Figure 17
Schematics of the tension/compression spring design.
Figure 18
Figure 18
Convergence curves of BOA on the tension/compression spring design.
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