1. MOEA/UE: A novel multi-objective evolutionary algorithm using a uniformly evolving scheme
- Author
-
Zhicang Wang, Hecheng Li, and Huifang Yu
- Subjects
0209 industrial biotechnology ,education.field_of_study ,Mathematical optimization ,Mutation operator ,Optimization problem ,Computer science ,Cognitive Neuroscience ,Computer Science::Neural and Evolutionary Computation ,Population ,MathematicsofComputing_NUMERICALANALYSIS ,Evolutionary algorithm ,02 engineering and technology ,Computer Science Applications ,Domain (software engineering) ,symbols.namesake ,020901 industrial engineering & automation ,Operator (computer programming) ,Artificial Intelligence ,Differential evolution ,0202 electrical engineering, electronic engineering, information engineering ,Taylor series ,symbols ,020201 artificial intelligence & image processing ,education - Abstract
In multi-objective optimization, the Pareto-optimal solutions must be widely and uniformly distributed on the Pareto-optimal front. When this condition is satisfied, decision makers can choose satisfactory solutions. In this paper, gaps in the evolving nondominated front are filled by a multi-objective evolutionary algorithm using a uniformly evolving scheme (MOEA/UE), thus generating a uniform and wide Pareto-optimal front. The MOEA/UE first calculates the distance between the points on the nondominated front and identifies those with relatively wide gaps. At the widely spaced points, the objective and constraint functions are approximated by linear functions expressed as Taylor series in auxiliary linear programs. Finally, the MOEA/UE solves these auxiliary linear programs and retains the optimal or intermediate solutions as the potentially superior individuals that will be selected in the next generation of the population. Convergence toward potentially superior solutions is accelerated by a mutation operator (a basic differential evolution operator). In constrained multi-objective optimization problems, the MOEA/UE implements a new constraint-handling strategy that pulls infeasible solutions into or nearby the feasible domain. Experimental results illustrate that MOEA/UE obtains wider and more uniform Pareto-optimal fronts and better Pareto-optimal solutions.
- Published
- 2021