A new population initialisation method based on the Pareto 80/20 rule for meta‐heuristic optimisation algorithms

Abstract

Abstract In this research, a new method for population initialisation in meta‐heuristic algorithms based on the Pareto 80/20 rule is presented. The population in a meta‐heuristic algorithm has two important tasks, including pushing the algorithm toward the real optima and preventing the algorithm from trapping in the local optima. Therefore, the starting point of a meta‐heuristic algorithm can have a significant impact on the performance and output results of the algorithm. In this research, using the Pareto 80/20 rule, an innovative and new method for creating an initial population in meta‐heuristic algorithms is presented. In this method, by using elitism, it is possible to increase the convergence of the algorithm toward the global optima, and by using the complete distribution of the population in the search spaces, the algorithm is prevented from trapping in the local optima. In this research, the proposed initialisation method was implemented in comparison with other initialisation methods using the cuckoo search algorithm. In addition, the efficiency and effectiveness of the proposed method in comparison with other well‐known initialisation methods using statistical tests and in solving a variety of benchmark functions including unimodal, multimodal, fixed dimensional multimodal, and composite functions as well as in solving well‐known engineering problems was confirmed.

Keywords

• Pareto optimisation
• search problems
• statistical testing
• metaheuristics