Proceedings Article10.1109/CEC.2016.7743989
A memetic algorithm for multi-objective optimization problems with interval parameters
Dunwei Gong,Zhuang Miao,Jing Sun +2 more
- 24 Jul 2016
- pp 1674-1681
6
TL;DR: A local search is embedded into an existing IMOEA, and a memetic algorithm for IMOPs is developed, and the empirical results indicate the effectiveness of the proposed algorithm.
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Abstract: Multi-objective optimization problems with interval parameters (IMOPs) are ubiquitous in real-world applications. The existing evolutionary algorithms for IMOPs (IMOEAs) require a large amount of function evaluations to generate an approximate Pareto front which is well converged and evenly distributed, and the generated front has uncertainties to a large extent. In this paper, a local search is embedded into an existing IMOEA, and a memetic algorithm for IMOPs is developed. The existing IMOEA is first employed to search the entire search space, and then the rate of changes of hypervolume is utilized to design an activation mechanism to specify when to conduct the local search. Finally, an initial population of the local search is created by taking the individuals with a large contribution to hypervolume and a small imprecision as the center, and the local search is implemented by taking the contribution to hypervolume as its fitness function. The proposed algorithm is applied to six benchmark IMOPs and an uncertain optimization problem of solar desalination, and compared with a typical IMOEA without the local search. The empirical results indicate the effectiveness of the proposed algorithm.
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Citations
Interval Multiobjective Optimization With Memetic Algorithms
TL;DR: This paper incorporates several local searches into an existing IMOEA, and proposes a memetic algorithm (MA) to tackle IMOPs, and experimental results demonstrate the applicability and effectiveness of the proposed MA.
130
A Multifactorial Evolutionary Algorithm for Multitasking Under Interval Uncertainties
TL;DR: A novel interval MFEA (IMFEA) is proposed to solve IMOOPs simultaneously using a single population of evolving individuals by promoting knowledge transfer for the greater synergistic search to improve the convergence speed and the quality of the optimal solution set.
78
Two-Stage Multi-Objective Meta-Heuristics for Environmental and Cost-Optimal Energy Refurbishment at District Level
TL;DR: In this paper, a two-stage optimization methodology is proposed in order to reduce the energy demand and fossil fuel consumption with an affordable investment cost at building level and minimize the total payback time while minimizing the Global Warming Potential (GWP) at district level.
25
AMOBH: Adaptive Multiobjective Black Hole Algorithm
TL;DR: Experimental results show that AMOBH has a good performance in terms of convergence rate, population diversity, population convergence, subpopulation obtention of different Pareto regions, and time complexity to the latter in most cases.
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