Evolutionary Multi-Criterion Optimization

Volume 1993 of the series Lecture Notes in Computer Science pp 385-399


A Hybrid Multi-objective Evolutionary Approach to Engineering Shape Design

  • Kalyanmoy DebAffiliated withKanpur Genetic Algorithms Laboratory (KanGAL), Indian Institute of Technology Kanpur
  • , Tushar GoelAffiliated withKanpur Genetic Algorithms Laboratory (KanGAL), Indian Institute of Technology Kanpur

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Evolutionary optimization algorithms work with a population of solutions, instead of a single solution. Since multi-objective optimization problems give rise to a set of Pareto-optimal solutions, evolutionary optimization algorithms are ideal for handling multi-objective optimization problems. Over many years of research and application studies have produced a number of efficient multi-objective evolutionary algorithms (MOEAs), which are ready to be applied to real-world problems. In this paper, we propose a practical approach, which will enable an user to move closer to the true Pareto-optimal front and simultaneously reduce the size of the obtained non-dominated solution set. The efficacy of the proposed approach is demonstrated in solving a number of mechanical shape optimization problems, including a simply-supported plate design, a cantilever plate design, a hoister design, and a bicycle frame design. The results are interesting and suggest immediate application of the proposed technique in more complex engineering design problems.