A Meta-Heuristic Search Algorithm based on Infrasonic Mating Displays in Peafowls
Journal of Advances in Mathematics and Computer Science,
Meta-heuristic techniques are important as they are used to find solutions to computationally intractable problems. Simplistic methods such as exhaustive search become computationally expensive and unreliable as the solution space for search algorithms increase. As no method is guaranteed to perform better than all others in all classes of optimization search problems, there is a need to constantly find new and/or adapt old search algorithms. This research proposes an Infrasonic Search Algorithm, inspired from the Gravitational Search Algorithm and the mating behaviour in peafowls. The Infrasonic Search Algorithm identified competitive solutions to 23 benchmark unimodal and multimodal test functions compared to the Genetic Algorithm, Particle Swarm Optimization Algorithm and the Gravitational Search Algorithm.
- infrasonic search algorithm
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Bet¨ul Sultan Yildiz, Ali Riza Yildiz. Moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes. Materialpruefung/Materials Testing. 2017;59(5):425-429.
Essam H. Houssein, Mohammed R. Saad, Fatma A. Hashim, Hassan Shaban, M. Hassaballah. L´evy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence. 2020;94:103731.
Zhong kai Feng, Wen jing Niu, Shuai Liu. Cooperation search algorithm: A novel metaheuristic evolutionary intelligence algorithm for numerical optimization and engineering optimization problems. Applied Soft Computing. 2021;98:106734.
Patrick Kenekayoro, Promise Mebine, Bodouowei Godswill Zipamone. Population based techniques for solving the student project allocation problem. International Journal of Applied Metaheuristic Computing (IJAMC). 2020;11(2):192-207.
Patrick Kenekayoro, Godswill Zipamone, Kenekayoro Patrick, Zipamone Godswill. Greedy ants colony optimization strategy for solving the curriculum based university course timetabling problem. British Journal of Mathematics & Computer Science. 2016;14(2):1-10.
Zahra Beheshti, Siti Mariyam, Hj Shamsuddin. A review of population-based meta-heuristic algorithm. International Journal of Advances in Soft Computing and its Applications. 2013;5(1):2074-8523.
Holland and J. Adaptation in natural and artificial systems : an introductory analysis with application to biology. Control and artificial intelligence; 1975.
Marco Dorigo, Gianni Di Caro. Ant colony optimization: A new meta-heuristic. Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999. IEEE Computer Society. 1999;2:1470-1477.
Gernard Venter, Jaroslaw Sobieszczanski-Sobieski. Particle swarm optimization. AIAA Journal. 2003;41(8):1583-1589.
Fred Glover. Tabu SearchPart I. ORSA Journal on Computing. 1989;1(3):190-206.
Mahdavi M, Fesanghary M, Damangir E. An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation. 2007;188(2):1567-1579.
Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, Meyarivan T. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. International Conference on Parallel Problem Solving From Nature. Berlin, Heidelberg, Springer. 2000;849-858.
Jorge Magalhaes-Mendes. A comparative study of crossover operators for genetic algorithms to solve the job shop scheduling problem. WSEAS Transactions on Computers. 2013;12(4):164- 173.
Noraini Mohd Razali, John Geraghty. Genetic algorithm performance with different selection strategies in solving tsp. Proceedings of the world congress on engineering. International Association of Engineers Hong Kong. 2011;2:1-6.
Angela R. Freeman, James F. Hare. Infrasound in mating displays: A peacock’s tale. Animal Behaviour. 2015;102:241-250.
Kashif Hussain, Mohd Najib Mohd Salleh, Shi Cheng, Rashid Naseem. Common benchmark functions for metaheuristic evaluation: A review. JOIV: International Journal on Informatics Visualization. 2017;1(4-2):218-223.
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