A new ABC-based multiobjective optimization algorithm with an improvement approach (IBMO: improved bee colony algorithm for multiobjective optimization)
| Autor: | Tahir Sağ, Mehmet Çunkaş |
|---|---|
| Přispěvatelé: | Selçuk Üniversitesi |
| Rok vydání: | 2016 |
| Předmět: |
Mathematical optimization
evolutionary algorithm Meta-optimization General Computer Science swarm intelligence Computer science Ant colony optimization algorithms 010102 general mathematics Evolutionary algorithm 02 engineering and technology Multiobjective optimization artificial bee colony optimization evolutionary algorithm swarm intelligence 01 natural sciences Multi-objective optimization Swarm intelligence Artificial bee colony algorithm artificial bee colony optimization 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0101 mathematics Electrical and Electronic Engineering Multi-swarm optimization Metaheuristic Multiobjective optimization |
| Zdroj: | Volume: 24, Issue: 4 2349-2373 Turkish Journal of Electrical Engineering and Computer Science |
| ISSN: | 1303-6203 1300-0632 |
| Popis: | WOS: 000374325800025 This paper presents a new metaheuristic algorithm based on the artificial bee colony (ABC) algorithm for multiobjective optimization problems. The proposed hybrid algorithm, an improved bee colony algorithm for multiobjective optimization called IBMO, combines the main ideas of the simple ABC with nondominated sorting strategy corresponding to the principal framework of multiobjective optimization such as Pareto-dominance and crowding distance. A fixed-sized external archive to store the nondominated solutions and an improvement procedure to promote the convergence to true Pareto front are used. The presented approach, IBMO, is compared with four representatives of the state-of-the-art algorithms: NSGA2, SPEA2, OMOPSO, and AbYSS. IBMO and the selected algorithms from specialized literature are applied to several multiobjective benchmark functions by considering the number of function evaluations. Then four quality indicators are employed for performance evaluations: general distance, spread, maximum spread, and hypervolume. The results show that the IBMO is superior to the other methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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