Behavior of EMO algorithms on many-objective optimization problems with correlated objectives

Ishibuchi, Hisao; Akedo, Naoya; Ohyanagi, Hiroyuki; Nojima, Yusuke

doi:10.1109/cec.2011.5949788

Cited by 52 publications

(29 citation statements)

References 26 publications

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“…Several studies have indicated that Pareto based algorithms scale poorly in MaOPs (Britto et al 2013;Britto and Pozo 2012;Ishibuchi et al 2011). The main reason for this is the number of non-dominated solutions which increases greatly with the number of objectives.…”

Section: Many-objective Optimizationmentioning

confidence: 99%

An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study

et al. 2017

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A variety of general strategies have been applied to enhance the performance of multi-objective optimization algorithms for many-objective optimization problems (those with more than three objectives). One of these strategies is to split the solutions to cover di↵erent regions (clusters) and apply an optimizer to each region with the aim of producing more diverse solutions and achieving a better distributed approximation of the Pareto front. However, the e↵ectiveness of clustering in this context depends on a number of issues, including the characteristics of the objective functions. In this paper we show how the choice of the clustering strategy can greatly influence the behavior an optimizer. We try to find a relation between the characteristics a of multi-objective optimization problem (MOP) and the e ciency of the use of a clustering type in its resolution. Using as a case study, the Iterated Multi-swarm (I-Multi), a recently introduced multi-objective particle swarm optimization (MOPSO) algorithm, we scrutinize the impact that clustering in di↵erent spaces (of variables, objectives, and a combination of both) can have on the approximations of the Pareto front. Furthermore, using two difficult function benchmarks of problems of up to 20 objectives, we evaluate the e↵ect of using di↵erent metrics for determining the similarity between the solutions during the clustering process. Our results confirm the important e↵ect of the clustering strategy on the behavior of multi-objective optimizers. Moreover, we present evidence that some problem characteristics can be used to select the most e↵ective clustering strategy, significantly improving the quality of the Pareto front approximations produced by I-Multi.

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Section: Many-objective Optimizationmentioning

confidence: 99%

An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study

et al. 2017

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“…The literature includes studies of pairwise relationships between objectives [2,6,20]. However, analysis techniques such as Kendall correlation [3] only manage to identify global relationships between objectives.…”

Section: Objectives Relationships In Multiobjective Optimisationmentioning

confidence: 99%

“…Purshouse and Fleming [5] discussed these techniques in their research into the relationships between objectives in MOPs. Other works that have used some of these techniques include Castro-Gutierrez et al [2] on multiobjective vehicle routing problems, and Ishibuchi et al [6] on many-objective problems with correlated objectives. One limitation of these techniques is that they are most suited to identify only pairwise relationships between objectives.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Objectives Relationships in Multiobjective Problems Using Trade-Off Region Maps

Pinheiro

Landa-Silva

Atkin

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Understanding the relationships between objectives in manyobjective optimisation problems is desirable in order to develop more effective algorithms. We propose a technique for the analysis and visualisation of complex relationships between many (three or more) objectives. This technique looks at conflicting, harmonious and independent objectives relationships from different perspectives. To do that, it uses correlation, trade-off regions maps and scatter-plots in a four step approach. We apply the proposed technique to a set of instances of the well-known multiobjective multidimensional knapsack problem. The experimental results show that with the proposed technique we can identify local and complex relationships between objectives, trade-offs not derived from pairwise relationships, gaps in the fitness landscape, and regions of interest. Such information can be used to tailor the development of algorithms.

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“…Due to the ability of local search to find local optima effectively over a relatively small part of the search space, genetic local search algorithms have been shown to be very suitable for solving complex multi-objective optimisation problems. Refer to (Beume et al 2007;Zhang and Li, 2007;Li and Zhang, 2009;Bader and Zitzler, 2011;Ishibuchi et al 2011;etc. ) for some recent multi-objective optimization algorithms.…”

Section: Related Workmentioning

confidence: 99%

A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems

2013

Ann Oper Res

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Abstract. This paper presents a new hybrid evolutionary algorithm to solve multi-objective multicast routing problems in telecommunication networks. The algorithm combines simulated annealing strategies and genetic local search, aiming at a more flexible and effective exploration and exploitation in the search space of the complex problem to find more non-dominated solutions in the Pareto Front. Due to the complex structure of the multicast tree, crossover and mutation operators have been specifically devised concerning the features and constraints in the problem. A new adaptive mutation probability based on simulated annealing is proposed in the hybrid algorithm to adaptively adjust the mutation rate according to the fitness of the new solution against the average quality of the current population during the evolution procedure. Two simulated annealing based search direction tuning strategies are applied to improve the efficiency and effectiveness of the hybrid evolutionary algorithm. Simulations have been carried out on some benchmark multi-objective multicast routing instances and a large amount of random networks with five real world objectives including cost, delay, link utilizations, average delay and delay variation in telecommunication networks. Experimental results demonstrate that both the simulated annealing strategies and the genetic local search within the proposed multi-objective algorithm, compared with other multi-objective evolutionary algorithms, can efficiently identify high quality non-dominated solution set for multi-objective multicast routing problems and outperform other conventional multi-objective evolutionary algorithms in the literature.

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Behavior of EMO algorithms on many-objective optimization problems with correlated objectives

Cited by 52 publications

References 26 publications

An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study

An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study

Analysis of Objectives Relationships in Multiobjective Problems Using Trade-Off Region Maps

A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems

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