Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations
AIR FORCE INST OF TECH WRIGHT-PATTERSONAFB OH SCHOOL OF ENGINEERING
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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 communitys 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 documents 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.
- Computer Programming and Software