A New Framework of Evolutionary Multi-Objective Algorithms with an Unbounded External Archive

Ishibuchi, Hisao; Pang, Lie Meng; Shang, Ke

doi:10.36227/techrxiv.11661276.v1

Cited by 8 publications

(7 citation statements)

References 36 publications

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“…The common feature of SPEA2 and MOEA/D-EAM, which gives them important advantage over NSGA-II, is their use of an external archive. This finding is consistent with recent studies, in which the NSGA-II equipped with external archive was used to improve optimization results of the canonical version [35,36]. In both SPEA2 and MOEA/D-EAM, the archive is used to construct the mating pool for recombination.…”

Section: Methodssupporting

confidence: 90%

Constrained Multi-Objective Optimization of Simulated Tree Pruning with Heterogeneous Criteria

Strnad

Kohek

2021

Applied Sciences

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Virtual pruning of simulated fruit tree models is a useful functionality provided by software tools for computer-aided horticultural education and research. It also enables algorithmic pruning optimization with respect to a set of quantitative objectives, which is important for analytical purposes and potential applications in automated pruning. However, the existing studies in pruning optimization focus on a single type of objective, such as light distribution within the crown. In this paper, we propose the use of heterogeneous objectives for discrete multi-objective optimization of simulated tree pruning. In particular, the average light intake, crown shape, and tree balance are used to observe the emergence of different pruning patterns in the non-dominated solution sets. We also propose the use of independent constraint objectives as a new mechanism to confine overfitting of solutions to individual pruning criteria. Finally, we perform the comparison of NSGA-II, SPEA2, and MOEA/D-EAM on this task. The results demonstrate that SPEA2 and MOEA/D-EAM, which use external solution archives, can produce better sets of non-dominated solutions than NSGA-II.

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Section: Methodssupporting

confidence: 90%

Constrained Multi-Objective Optimization of Simulated Tree Pruning with Heterogeneous Criteria

Strnad

Kohek

2021

Applied Sciences

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“…However, as shown in [19], the final population is not always a good solution set. In fact, it is often the case for many EMO algorithms that some solutions in the final population are dominated by other (b) New EMO framework proposed in [18] Fig. 1.…”

Section: A Motivation Of This Workmentioning

confidence: 99%

“…2) A new EMO framework based on subset selection: In order to solve the above issue in the standard EMO framework, a new EMO framework is proposed in [18], which is illustrated in Fig. 1 (b).…”

Section: A Motivation Of This Workmentioning

confidence: 99%

Benchmarking Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization

Shang¹,

Shu²,

Ishibuchi³

et al. 2022

Preprint

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In the evolutionary multi-objective optimization (EMO) field, the standard practice is to present the final population of an EMO algorithm as the output. However, it has been shown that the final population often includes solutions which are dominated by other solutions generated and discarded in previous generations. Recently, a new EMO framework has been proposed to solve this issue by storing all the non-dominated solutions generated during the evolution in an archive and selecting a subset of solutions from the archive as the output. The key component in this framework is the subset selection from the archive which usually stores a large number of candidate solutions. However, most studies on subset selection focus on small candidate solution sets for environmental selection. There is no benchmark test suite for large-scale subset selection. This paper aims to fill this research gap by proposing a benchmark test suite for subset selection from large candidate solution sets, and comparing some representative methods using the proposed test suite. The proposed test suite together with the benchmarking studies provides a baseline for researchers to understand, use, compare, and develop subset selection methods in the EMO field.

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“…Even though the elitist framework is much more efficient than the non-elitist framework, it still has a difficulty. As discussed in [14], good solutions can be deleted during the generation update phase. Since the population size (and the archive size) is pre-specified, some solutions must be discarded during the generation update phase.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, it was proposed to use an unbounded external archive in the design of EMO algorithms [14]. The idea is to present a solution set selected from all the examined solutions to the decision maker.…”

Section: Introductionmentioning

confidence: 99%

Decomposition-Based Multi-Objective Evolutionary Algorithm Design Under Two Algorithm Frameworks

2020

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The development of efficient and effective evolutionary multi-objective optimization (EMO) algorithms has been an active research topic in the evolutionary computation community. Over the years, many EMO algorithms have been proposed. The existing EMO algorithms are mainly developed based on the final population framework. In the final population framework, the final population of an EMO algorithm is presented to the decision maker. Thus, it is required that the final population produced by an EMO algorithm is a good solution set. Recently, the use of solution selection framework was suggested for the design of EMO algorithms. This framework has an unbounded external archive to store all the examined solutions. A pre-specified number of solutions are selected from the archive as the final solutions presented to the decision maker. When the solution selection framework is used, EMO algorithms can be designed in a more flexible manner since the final population is not necessarily to be a good solution set. In this paper, we examine the design of MOEA/D under these two frameworks. We use an offline genetic algorithm-based hyper-heuristic method to find the optimal configuration of MOEA/D in each framework. The DTLZ and WFG test suites and their minus versions are used in our experiments. The experimental results suggest the possibility that a more flexible, robust and high-performance MOEA/D algorithm can be obtained when the solution selection framework is used.

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A New Framework of Evolutionary Multi-Objective Algorithms with an Unbounded External Archive

Cited by 8 publications

References 36 publications

Constrained Multi-Objective Optimization of Simulated Tree Pruning with Heterogeneous Criteria

Constrained Multi-Objective Optimization of Simulated Tree Pruning with Heterogeneous Criteria

Benchmarking Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization

Decomposition-Based Multi-Objective Evolutionary Algorithm Design Under Two Algorithm Frameworks

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