In this study,a completely different approach to optimization is introduced through the development of a novel metaheuristic algorithm called the Barber Optimization algorithm(BaOA).Inspired by the human interactions ...
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In this study,a completely different approach to optimization is introduced through the development of a novel metaheuristic algorithm called the Barber Optimization algorithm(BaOA).Inspired by the human interactions between barbers and customers,BaOA captures two key processes:the customer’s selection of a hairstyle and the detailed refinement during the *** processes are translated into a mathematical framework that forms the foundation of BaOA,consisting of two critical phases:exploration,representing the creative selection process,and exploitation,which focuses on refining details for *** performance of BaOA is evaluated using 52 standard benchmark functions,including unimodal,high-dimensional multimodal,fixed-dimensional multimodal,and the Congress on Evolutionary Computation(CEC)2017 test *** comprehensive assessment highlights BaOA’s ability to balance exploration and exploitation effectively,resulting in high-quality solutions.A comparative analysis against twelve widely known metaheuristic algorithms further demonstrates BaOA’s superior performance,as it consistently delivers better results across most benchmark *** validate its real-world applicability,BaOA is tested on four engineering design problems,illustrating its capability to address practical challenges with remarkable *** results confirm BaOA’s versatility and reliability as an optimization *** study not only introduces an innovative algorithm but also establishes its effectiveness in solving complex problems,providing a foundation for future research and applications in diverse scientific and engineering domains.
Dragon boat racing, a popular aquatic folklore team sport, is traditionally held during the Dragon Boat Festival. Inspired by this event, we propose a novel human-based meta-heuristic algorithm called dragon boat opti...
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Dragon boat racing, a popular aquatic folklore team sport, is traditionally held during the Dragon Boat Festival. Inspired by this event, we propose a novel human-based meta-heuristic algorithm called dragon boat optimization (DBO) in this paper. It models the unique behaviours of each crew member on the dragon boat during the race by introducing social psychology mechanisms (social loafing, social incentive). Throughout this process, the focus is on the interaction and collaboration among the crew members, as well as their decision-making in various situations. During each iteration, DBO implements different state updating strategies. By accurately modelling the crew's behaviour and employing adaptive state update strategies, DBO consistently achieves high optimization performance, as validated by comprehensive testing on 29 benchmark functions and 2 structural design problems. Experimental results indicate that DBO outperforms 7 and 16 state-of-the-art meta-heuristic algorithms across these test functions and problems, respectively.
This paper introduces a newmetaheuristic algorithmcalledMigration algorithm(MA),which is helpful in solving optimization *** fundamental inspiration of MA is the process of human migration,which aims to improve job,ed...
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This paper introduces a newmetaheuristic algorithmcalledMigration algorithm(MA),which is helpful in solving optimization *** fundamental inspiration of MA is the process of human migration,which aims to improve job,educational,economic,and living conditions,and so *** of the proposed MAis presented in two phases to empower the proposed approach in exploration and exploitation during the search *** the exploration phase,the algorithm population is updated based on the simulation of choosing the migration destination among the available *** the exploitation phase,the algorithm population is updated based on the efforts of individuals in the migration destination to adapt to the new environment and improve their ***’s performance is evaluated on fifty-two standard benchmark functions consisting of unimodal and multimodal types and the CEC 2017 test *** addition,MA’s results are compared with the performance of twelve well-known metaheuristic *** optimization results show the proposed MA approach’s high ability to balance exploration and exploitation to achieve suitable solutions for optimization *** analysis and comparison of the simulation results show that MA has provided superior performance against competitor algorithms in most benchmark ***,the implementation of MA on four engineering design problems indicates the effective capability of the proposed approach in handling optimization tasks in real-world applications.
Metaheuristics are a class of algorithms with some intelligence and self-learning capabilities to find solutions to difficult combinatorial problems. Although the promised solutions are not necessarily globally optima...
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ISBN:
(纸本)9783030588175;9783030588168
Metaheuristics are a class of algorithms with some intelligence and self-learning capabilities to find solutions to difficult combinatorial problems. Although the promised solutions are not necessarily globally optimal, they are computationally economical. In general, these types of algorithms have been created by imitating intelligent processes and behaviors observed in nature, sociology, psychology and other disciplines. Metaheuristic-based search and optimization is currently widely used for decision making and problem solving in different contexts. The inspiration for metaheuristic algorithms are mainly based on nature's behaviour or biological behaviour. Designing a good metaheurisitcs is making a proper trade-off between two forces: Exploration and exploitation. It is one of the most basic dilemmas that both individuals and organizations constantly are facing. But there is a little researched branch, which corresponds to the techniques based on the social behavior of people or communities, which are called Social-inspired. In this paper we explain and compare two socio-inspired metaheuristics solving a benchmark combinatorial problem.
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