Flower Pollination Algorithm: A Novel Approach for Multiobjective Optimization

Abstract

Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this paper, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis are highlighted and discussed.

17 pages 8 figures. arXiv admin note: substantial text overlap with arXiv:1404.0695

References in corpus (4)

• Bat Algorithm: A Novel Approach for Global Engineering Optimization
• Flower Pollination Algorithm for Global Optimization
• Levy Flights, Non-local Search and Simulated Annealing
• Multi-objective Flower Algorithm for Optimization

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