A New Fuzzy Dominance GA Applied to Solve Many-Objective Optimization Problem

Wang, Gaoping; Wu, Jianjun

doi:10.1109/icicic.2007.49

Cited by 8 publications

(2 citation statements)

References 6 publications

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“…Evolutionary optimization methods, such as NSGA-II, have been used to optimize fuzzy systems by optimally partitioning the universe of discourse of fuzzy variables and extracting an optimal set of fuzzy rules [5], [47], [57], [58]. Similarly, fuzzy systems have also been used in the past to improve the overall performance of evolutionary optimization methods [32]- [34], [38], [59], [60]. However, the role of fuzzy logic in EAs has been limited either to the selection of a suitable solution from the set of PF solutions, or to incorporate user preference to guide the convergence of EAs.…”

Section: Fuzzy Based Sorting Genetic Algorithmsmentioning

confidence: 99%

Evolutionary Optimization Using Equitable Fuzzy Sorting Genetic Algorithm (EFSGA)

2019

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This paper presents a fuzzy dominance-based analytical sorting method as an advancement to the existing multi-objective evolutionary algorithms (MOEA). Evolutionary algorithms (EAs), on account of their sorting schemes, may not establish clear discrimination amongst solutions while solving many-objective optimization problems. Moreover, these algorithms are also criticized for issues such as uncertain termination criterion and difficulty in selecting a final solution from the set of Pareto optimal solutions for practical purposes. An alternate approach, referred here as equitable fuzzy sorting genetic algorithm (EFSGA), is proposed in this paper to address these vital issues. Objective functions are defined as fuzzy objectives and competing solutions are provided an overall activation score (OAS) based on their respective fuzzy objective values. Subsequently, OAS is used to assign an explicit fuzzy dominance ranking to these solutions for improved sorting process. Benchmark optimization problems, used as case studies, are optimized using proposed algorithm with three other prevailing methods. Performance indices are obtained to evaluate various aspects of the proposed algorithm and present a comparison with existing methods. It is shown that the EFSGA exhibits strong discrimination ability and provides unambiguous termination criterion. The proposed approach can also help user in selecting final solution from the set of Pareto optimal solutions. INDEX TERMS Multi-objective optimization, evolutionary algorithms, equitable fuzzy sorting genetic algorithm.

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Section: Fuzzy Based Sorting Genetic Algorithmsmentioning

confidence: 99%

Evolutionary Optimization Using Equitable Fuzzy Sorting Genetic Algorithm (EFSGA)

2019

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“…cannot be efficiently used if the formulation results in a many-objective problem. Modifying the selection pressure through the use of secondary metrics (e.g., substitute distance measures [73] [74], average rank domination [75], fuzzy dominance [76], [77], ǫ-dominance [78] [79], adaptive ǫ-ranking [80] etc.) has so far exhibited only partial success in solving such problems.…”

Section: Introductionmentioning

confidence: 99%

Six-Sigma Robust Design Optimization Using a Many-Objective Decomposition-Based Evolutionary Algorithm

Asafuddoula

Singh

Ray

2015

IEEE Trans. Evol. Computat.

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Robust design optimization aims to find solutions that are competent and reliable under given uncertainties. While such uncertainties can emerge from a number of sources (imprecise variable values, errors in performance estimates, varying environmental conditions etc.), this study focuses on problems where uncertainties emanate from design variables. In commercial designs, being reliable is often of more practical value than being globally best (but unreliable). Robust optimization poses three key challenges (a) appropriate formulation of the problem, (b) accurate estimation of the 'robustness' measure and (c) efficient means to identify the set of tradeoff robust solutions with an affordable computational cost. In this paper, four different problem formulations for robust optimization are presented and analyzed. The proposed formulations offer a set of tradeoff solutions with robustness from two perspectivesfeasibility robustness, i.e., robustness against failure and performance robustness, i.e., robustness assuring good performance. The approach also provides means to identify critical constraints or performance functions that affect the overall robustness. The problem is posed as a many-objective optimization problem and a decomposition based evolutionary approach is used for solving it. The performance of the proposed approach and the consequences of using different formulations are illustrated using two numerical examples and four engineering problems.

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