Publication | Closed Access
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
1.2K
Citations
0
References
1999
Year
Unknown Venue
Building BlocksEngineeringIndustrial EngineeringSoftware EngineeringEvolutionary AlgorithmsIntelligent SystemsEvolutionary Multimodal OptimizationOperations ResearchSystems EngineeringHybrid Optimization TechniqueBiostatisticsEvolution-based MethodMoea IdentificationMultiobjective Evolutionary AlgorithmsDesignComputer ScienceEvolutionary ProgrammingEvolutionary BiologySoftware TestingEvolutionary DesignConsistent Moea Terminology
The MOEA field suffers from limited, poorly documented test suites, and this paper offers a comprehensive, up‑to‑date overview and introductory guide for newcomers. The study aims to systematically organize, analyze, and extend MOEA research by introducing building‑block concepts for MOPs and proposing a database‑based evaluation framework. The authors employ a unified terminology, tabulate key MOEA factors, generate guideline‑based test suites, and develop a solution‑database evaluation framework. The quantitative and qualitative analysis yields conclusions on various MOEA‑related issues, forming a foundation for future research.
Abstract : This research organizes, presents, and analyzes contemporary Multiobjective Evolutionary Algorithm (MOEA) research and associated Multiobjective Optimization Problems (MOPs). Using a consistent MOEA terminology and notation, each cited MOEAs' key factors are presented in tabular form for ease of MOEA identification and selection. A detailed quantitative and qualitative MOEA analysis is presented, providing a basis for conclusions about various MOEA-related issues. The traditional notion of building blocks is extended to the MOP domain in an effort to develop more effective and efficient MOEAs. Additionally, the MOEA community's limited test suites contain various functions whose origins and rationale for use are often unknown. Thus, using general test suite guidelines appropriate MOEA test function suites are substantiated and generated. An experimental methodology incorporating a solution database and appropriate metrics is offered as a proposed evaluation framework allowing absolute comparisons of specific MOEA approaches. Taken together, this document's classifications, analyses, and new innovations present a complete, contemporary view of current MOEA state of the art and possible future research. Researchers with basic EA knowledge may also use part of it as a largely self-contained introduction to MOEAs.