An <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>R</mml:mi><mml:mn fontstyle="italic">2</mml:mn></mml:math> Indicator and Decomposition Based Steady-State Evolutionary Algorithm for Many-Objective Optimization

Li, Fēi; Liu, Jianchang; Huang, Pei-Qiu; Shi, Huaitao

doi:10.1155/2018/1435463

Cited by 3 publications

(1 citation statement)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fei et al [34] proposed an evolutionary method based on the R2 indicator and decomposition procedure to achieve a well-converged and -distributed Pareto front. The method maximizes the separability of populations by (1) adopting the objective space population strategy and (2) ensuring diversity by generating a set of reference vectors using a twolayer reference vector generation procedure.…”

Section: ) R2 Indicator-based Methodsmentioning

confidence: 99%

Methods That Optimize Multi-Objective Problems: A Survey and Experimental Evaluation

Taha

2020

IEEE Access

View full text Add to dashboard Cite

Most current multi-optimization survey papers classify methods into broad objective categories and do not draw clear boundaries between the specific techniques employed by these methods. This may lead to the misclassification of unrelated methods/techniques into the same objective category. Moreover, most of these survey papers classify algorithms as independent of the specific techniques they employ. Toward this end, we introduce in this survey paper a methodology-based taxonomy that classifies multi-optimization methods into hierarchically nested, fine-grained, and specific classes. We provide a methodological taxonomy to classify methods into the following hierarchical fashion: objective categories© objective functions©optimization methods©optimization sub-methods. We introduce a comprehensive survey on the methods that are contained under each optimization method, the optimization methods contained under each objective function, and objective functions contained under each objective category. We selected the objective functions that should be maximized for solving most real-word multi-objective optimization problems, which are pairs of the following: partitions separability, internal density, dynamic similarity, and structural similarity. For each optimization method, we surveyed the various algorithms in literature that pertain to the method. We experimentally compared and ranked the optimization methods that fall under each objective function, the objective functions that fall under each objective category, and the objective categories used for solving a specific optimization problem.INDEX TERMS Multi-objective optimization, multi-objective problem, multi-objective evolutionary algorithm, objective function.Our methodology-based taxonomy enables a researcher reader to gain knowledge about the following:1) The very specific method under which the researcher's proposed algorithm falls.2) The objective's category and function under which the researcher's proposed method falls.3) The advantages and limitations of the method, objective category, and objective function under which the researcher's proposed algorithm falls. 4) The research works in literature that are the most comparable with the researcher's proposed algorithm. B. CURRENT SURVEY PAPERS ON THE TOPICCurrent survey papers can be classified into the following broad categories: multi-objective and many-objective methods. Multi-objective papers can be further classified into evolutionary-based and decomposition-based methods. In this Subsection, we outline the most notable survey papers based on the mentioned classifications.

show abstract

Section: ) R2 Indicator-based Methodsmentioning

confidence: 99%