A Pareto-Based Hybrid Whale Optimization Algorithm with Tabu Search for Multi-Objective Optimization

Multi-Objective Problems (MOPs) are common real-life problems that can be found in different fields, such as bioinformatics and scheduling. Pareto Optimization (PO) is a popular method for solving MOPs, which optimizes all objectives simultaneously. It provides an effective way to evaluate the quali...

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Vydáno v:Algorithms Ročník 12; číslo 12; s. 261
Hlavní autoři: AbdelAziz, Amr Mohamed, Soliman, Taysir Hassan A., Ghany, Kareem Kamal A., Sewisy, Adel Abu El-Magd
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 04.12.2019
Témata:
Algorithms
Bioinformatics
Diversification
Genetic algorithms
Heuristic
Methods
Mopping
Multiple objective analysis
Objectives
Optimization algorithms
Pareto optimization
Population
Search methods
Solution space
Swarm intelligence
Tabu search
ISSN:1999-4893, 1999-4893
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Shrnutí:Multi-Objective Problems (MOPs) are common real-life problems that can be found in different fields, such as bioinformatics and scheduling. Pareto Optimization (PO) is a popular method for solving MOPs, which optimizes all objectives simultaneously. It provides an effective way to evaluate the quality of multi-objective solutions. Swarm Intelligence (SI) methods are population-based methods that generate multiple solutions to the problem, providing SI methods suitable for MOP solutions. SI methods have certain drawbacks when applied to MOPs, such as swarm leader selection and obtaining evenly distributed solutions over solution space. Whale Optimization Algorithm (WOA) is a recent SI method. In this paper, we propose combining WOA with Tabu Search (TS) for MOPs (MOWOATS). MOWOATS uses TS to store non-dominated solutions in elite lists to guide swarm members, which overcomes the swarm leader selection problem. MOWOATS employs crossover in both intensification and diversification phases to improve diversity of the population. MOWOATS proposes a new diversification step to eliminate the need for local search methods. MOWOATS has been tested over different benchmark multi-objective test functions, such as CEC2009, ZDT, and DTLZ. Results present the efficiency of MOWOATS in finding solutions near Pareto front and evenly distributed over solution space.
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ISSN:1999-4893
1999-4893
DOI:10.3390/a12120261