TLMPA: Teaching-learning-based Marine Predators algorithm

Author:

Zhong Keyu, ,Luo Qifang,Zhou Yongquan,Jiang Ming, , ,

Abstract

<abstract> <p>Marine Predators algorithm (MPA) is a newly proposed nature-inspired metaheuristic algorithm. The main inspiration of this algorithm is based on the extensive foraging strategies of marine organisms, namely Lévy movement and Brownian movement, both of which are based on random strategies. In this paper, we combine the marine predator algorithm with Teaching-learning-based optimization algorithm, and propose a hybrid algorithm called Teaching-learning-based Marine Predator algorithm (TLMPA). Teaching-learning-based optimization (TLBO) algorithm consists of two phases: the teacher phase and the learner phase. Combining these two phases with the original MPA enables the predators to obtain prey information for foraging by learning from teachers and interactive learning, thus greatly increasing the encounter rate between predators and prey. In addition, effective mutation and crossover strategies were added to increase the diversity of predators and effectively avoid premature convergence. For performance evaluation TLMPA algorithm, it has been applied to IEEE CEC-2017 benchmark functions and four engineering design problems. The experimental results show that among the proposed TLMPA algorithm has the best comprehensive performance and has more outstanding performance than other the state-of-the-art metaheuristic algorithms in terms of the performance measures.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

General Mathematics

Reference85 articles.

1. J. H. Holland, Genetic algorithms, Sci. Am., 267 (1992), 66-72.

2. J. Kennedy, R. Eberhart, Particle swarm optimization, Perth, WA, Australia: Proceedings of IEEE International Conference on Neural Networks, 1995.

3. R. Storn, K. Price, Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim., 11 (1997), 341-359.

4. K. V. Price, Differential evolution: A fast and simple numerical optimizer, Berkeley, CA, USA: Proceedings of North American Fuzzy Information Processing, 1996.

5. D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm, J. Global Optim., 39 (2007), 459-471.

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