Cat and Mouse Based Optimizer: A New Nature-Inspired Optimization Algorithm
Numerous optimization problems designed in different branches of science and the real world must be solved using appropriate techniques. Population-based optimization algorithms are some of the most important and practical techniques for solving optimization problems. In this paper, a new optimizati...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/15/5214 |
| _version_ | 1797411260171550720 |
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| author | Mohammad Dehghani Štěpán Hubálovský Pavel Trojovský |
| author_facet | Mohammad Dehghani Štěpán Hubálovský Pavel Trojovský |
| author_sort | Mohammad Dehghani |
| collection | DOAJ |
| description | Numerous optimization problems designed in different branches of science and the real world must be solved using appropriate techniques. Population-based optimization algorithms are some of the most important and practical techniques for solving optimization problems. In this paper, a new optimization algorithm called the Cat and Mouse-Based Optimizer (CMBO) is presented that mimics the natural behavior between cats and mice. In the proposed CMBO, the movement of cats towards mice as well as the escape of mice towards havens is simulated. Mathematical modeling and formulation of the proposed CMBO for implementation on optimization problems are presented. The performance of the CMBO is evaluated on a standard set of objective functions of three different types including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. The results of optimization of objective functions show that the proposed CMBO has a good ability to solve various optimization problems. Moreover, the optimization results obtained from the CMBO are compared with the performance of nine other well-known algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching-Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), Tunicate Swarm Algorithm (TSA), and Teamwork Optimization Algorithm (TOA). The performance analysis of the proposed CMBO against the compared algorithms shows that CMBO is much more competitive than other algorithms by providing more suitable quasi-optimal solutions that are closer to the global optimal. |
| first_indexed | 2024-03-09T04:42:30Z |
| format | Article |
| id | doaj.art-ef374c73de0347b7936650cd42dc732f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:42:30Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ef374c73de0347b7936650cd42dc732f2023-12-03T13:19:21ZengMDPI AGSensors1424-82202021-07-012115521410.3390/s21155214Cat and Mouse Based Optimizer: A New Nature-Inspired Optimization AlgorithmMohammad Dehghani0Štěpán Hubálovský1Pavel Trojovský2Department of Mathematics, Faculty of Science, University of Hradec Králové, 500 03 Hradec Králové, Czech RepublicDepartment of Applied Cybernetics, Faculty of Science, University of Hradec Králové, 500 03 Hradec Králové, Czech RepublicDepartment of Mathematics, Faculty of Science, University of Hradec Králové, 500 03 Hradec Králové, Czech RepublicNumerous optimization problems designed in different branches of science and the real world must be solved using appropriate techniques. Population-based optimization algorithms are some of the most important and practical techniques for solving optimization problems. In this paper, a new optimization algorithm called the Cat and Mouse-Based Optimizer (CMBO) is presented that mimics the natural behavior between cats and mice. In the proposed CMBO, the movement of cats towards mice as well as the escape of mice towards havens is simulated. Mathematical modeling and formulation of the proposed CMBO for implementation on optimization problems are presented. The performance of the CMBO is evaluated on a standard set of objective functions of three different types including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. The results of optimization of objective functions show that the proposed CMBO has a good ability to solve various optimization problems. Moreover, the optimization results obtained from the CMBO are compared with the performance of nine other well-known algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching-Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), Tunicate Swarm Algorithm (TSA), and Teamwork Optimization Algorithm (TOA). The performance analysis of the proposed CMBO against the compared algorithms shows that CMBO is much more competitive than other algorithms by providing more suitable quasi-optimal solutions that are closer to the global optimal.https://www.mdpi.com/1424-8220/21/15/5214optimizationpopulation-basedstochasticcat and mouseoptimization problem |
| spellingShingle | Mohammad Dehghani Štěpán Hubálovský Pavel Trojovský Cat and Mouse Based Optimizer: A New Nature-Inspired Optimization Algorithm Sensors optimization population-based stochastic cat and mouse optimization problem |
| title | Cat and Mouse Based Optimizer: A New Nature-Inspired Optimization Algorithm |
| title_full | Cat and Mouse Based Optimizer: A New Nature-Inspired Optimization Algorithm |
| title_fullStr | Cat and Mouse Based Optimizer: A New Nature-Inspired Optimization Algorithm |
| title_full_unstemmed | Cat and Mouse Based Optimizer: A New Nature-Inspired Optimization Algorithm |
| title_short | Cat and Mouse Based Optimizer: A New Nature-Inspired Optimization Algorithm |
| title_sort | cat and mouse based optimizer a new nature inspired optimization algorithm |
| topic | optimization population-based stochastic cat and mouse optimization problem |
| url | https://www.mdpi.com/1424-8220/21/15/5214 |
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