Many-objective BAT algorithm
| Autor: | Adeem Ali Anwar, Uzman Perwaiz, Irfan Younas |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Convergent Evolution
Optimization problem Research Facilities Computer science Evolutionary algorithm Social Sciences 02 engineering and technology Information Centers Mathematical and Statistical Techniques 0202 electrical engineering electronic engineering information engineering Psychology Multidisciplinary Fitness function Animal Behavior Archives Mathematical Models Applied Mathematics Simulation and Modeling Rank (computer programming) Pareto principle Swarm behaviour Random walk Computer Heuristics Physical Sciences Medicine 020201 artificial intelligence & image processing Algorithms Research Article Optimization Mathematical optimization Evolutionary Processes Science Research and Analysis Methods Set (abstract data type) 020204 information systems Genetic algorithm Computational Techniques Bat algorithm Behavior Evolutionary Biology Genetic Algorithms Biology and Life Sciences Echolocation Random Walk Neural Networks Computer Evolutionary Algorithms Evolutionary Computation Zoology Mathematics |
| Zdroj: | PLoS ONE, Vol 15, Iss 6, p e0234625 (2020) PLoS ONE |
| ISSN: | 1932-6203 |
| Popis: | In many objective optimization problems (MaOPs), more than three distinct objectives are optimized. The challenging part in MaOPs is to get the Pareto approximation (PA) with high diversity and good convergence. In Literature, in order to solve the issue of diversity and convergence in MaOPs, many approaches are proposed using different multi objective evolutionary algorithms (MOEAs). Moreover, to get better results, the researchers use the sets of reference points to differentiate the solutions and to model the search process, it further evaluates and selects the non-dominating solutions by using the reference set of solutions. Furthermore, this technique is used in some of the swarm-based evolutionary algorithms. In this paper, we have used some effective adaptations of bat algorithm with the previous mentioned approach to effectively handle the many objective problems. Moreover, we have called this algorithm as many objective bat algorithm (MaOBAT). This algorithm is a biologically inspired algorithm, which uses echolocation power of micro bats. Each bat represents a complete solution, which can be evaluated based on the problem specific fitness function and then based on the dominance relationship, non-dominated solutions are selected. In proposed MaOBAT, dominance rank is used as dominance relationship (dominance rank of a solution means by how many other solutions a solution dominated). In our proposed strategy, dynamically allocated set of reference points are used, allowing the algorithm to have good convergence and high diversity pareto fronts (PF). The experimental results show that the proposed algorithm has significant advantages over several state-of-the-art algorithms in terms of the quality of the solution. |
| Databáze: | OpenAIRE |
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