Incorporation of Scalarizing Fitness Functions into Evolutionary Multiobjective Optimization Algorithms

Ishibuchi, Hisao; Doi, Tomoharu; Nojima, Yusuke

doi:10.1007/11844297_50

Cited by 51 publications

(33 citation statements)

References 15 publications

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“…Non-Pareto dominance based approach include techniques like indicator function [24-26, 28-30, 175], scalarizing function (a kind of weighted sum approach) [31][32][33][34][35][36][37], and preference information [27,[38][39][40][41][42][43][44]. Out of the above three approaches indicator function approach is widely used.…”

Section: Preference Ordering Approachmentioning

confidence: 99%

Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis

Mishra

Dehuri

Cho

2015

Multi-Objective Swarm Intelligence

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This paper systematically presents the Swarm Intelligence (SI) methods for optimization of multiple and many objective problems. The fundamental difference of Multiple and Many Objective Optimization problems have been studied very rigorously. The three forefront swarm intelligence methods, i.e., Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony Optimization (ABC) has been deeply studied to understand their ways of solving multiple and many objective problems distinctly. A pragmatic topical study on the behavior of real ants, bird flocks, and honey bees in solving EEG signal analysis completes the survey followed by discussion and extensive number of relevant references.

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Section: Preference Ordering Approachmentioning

confidence: 99%

Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis

Mishra

Dehuri

Cho

2015

Multi-Objective Swarm Intelligence

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“…Hansen (2000) considered the use of a Chebyshev program, which means optimizing a particular scalarizing function for a local search in the tabu search (Glover 1989) and which has been tested to be superior in handling a multiobjective TSP. Ishibuchi et al (2006) proposed an idea of probabilistically using a scalarizing fitness function (weighted sum) and the NSGA-II fitness evaluation mechanism during parent selection in EMO. Computational experiments on 0/1 knapsack problems with two, three and four objectives showed improvement in the performance of EMO algorithms.…”

Section: Previous Studies On Hybrid Emo Approachesmentioning

confidence: 99%

Improving convergence of evolutionary multi-objective optimization with local search: a concurrent-hybrid algorithm

2011

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A local search method is often introduced in an evolutionary optimization algorithm, to enhance its speed and accuracy of convergence to optimal solutions. In multiobjective optimization problems, the implementation of local search is a non-trivial task, as determining a goal for local search in presence of multiple conflicting objectives becomes a difficult task. In this paper, we borrow a multiple criteria decision making concept of employing a reference point based approach of minimizing an achievement scalarizing function and integrate it as a search operator with a concurrent approach in an evolutionary multi-objective algorithm. Simulation results of the new concurrent-hybrid algorithm on several two to four-objective problems compared to a serial approach, clearly show the importance of local search in aiding a computationally faster and accurate convergence to the Pareto optimal front.

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“…Motivated by these studies on multiobjectivization, we examined the use of EMO algorithms to optimize the sum of multiple objectives in our former studies [8], [10]. We obtained promising results when we used NSGA-II [3] to optimize the simple sum fitness function for a two-objective 500-item (i.e., 2-500) knapsack problem of Zitzler & Thiele [19].…”

Section: Introductionmentioning

confidence: 93%

“…High performance of MOGLS of Jaszkiewicz [12] was reported [1], [13], [16]. The weighted sum fitness function was also used in hybrid or multi-stage EMO algorithms (e.g., see [8], [10], [16]). …”

Section: Scalarizing Functionsmentioning

confidence: 99%

“…In our former studies [8], [10], we proposed a hybrid EMO algorithm where a weighted sum fitness function was probabilistically used in NSGA-II. We introduced two probabilities PPS and PGU, which specified how often the weighted sum fitness function was used for parent selection and generation update in the hybrid EMO algorithm, respectively.…”

Section: Application Of a Hybrid Emo Algorithmmentioning

confidence: 99%

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Optimization of Scalarizing Functions Through Evolutionary Multiobjective Optimization

Ishibuchi

Nojima

Lecture Notes in Computer Science

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Abstract. This paper proposes an idea of using evolutionary multiobjective optimization (EMO) to optimize scalarizing functions. We assume that a scalarizing function to be optimized has already been generated from an original multiobjective problem. Our task is to optimize the given scalarizing function. In order to efficiently search for its optimal solution without getting stuck in local optima, we generate a new multiobjective problem to which an EMO algorithm is applied. The point is to specify multiple objectives, which are similar to but different from the scalarizing function, so that the location of the optimal solution is near the center of the Pareto front of the generated multiobjective problem. The use of EMO algorithms helps escape from local optima. It also helps find a number of alternative solutions around the optimal solution. Difficulties of Pareto ranking-based EMO algorithms in the handling of many objectives are avoided by the use of similar objectives. In this paper, we first demonstrate that the performance of EMO algorithms as single-objective optimizers of scalarizing functions highly depends on the choice of multiple objectives. Based on this observation, we propose a specification method of multiple objectives for the optimization of a weighted sum fitness function. Experimental results show that our approach works very well in the search for not only a single optimal solution but also a number of good alternative solutions around the optimal solution. Next we evaluate the performance of our approach in comparison with a hybrid EMO algorithm where a single-objective fitness evaluation scheme is probabilistically used in an EMO algorithm. Then we show that our approach can be also used to optimize other scalarizing functions (e.g., those based on constraint conditions and reference solutions). Finally we show that our approach is applicable not only to scalarizing functions but also other single-objective optimization problems.

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Incorporation of Scalarizing Fitness Functions into Evolutionary Multiobjective Optimization Algorithms

Cited by 51 publications

References 15 publications

Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis

Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis

Improving convergence of evolutionary multi-objective optimization with local search: a concurrent-hybrid algorithm

Optimization of Scalarizing Functions Through Evolutionary Multiobjective Optimization

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