Despite progress in multiobjective evolutionary algorithms (MOEAs) research, their efficacy in real-world scenarios remains unclear. This article introduces a diagnostic benchmarking framework to evaluate MOEAs, comprising (1) flexible MOEA construction software, (2) performance evaluation metrics and (3) real-world applications for benchmarking, reflecting diverse mathematical challenges. Utilizing this framework, NSGA-II, NSGA-III, RVEA, MOEA/D and Borg MOEA were evaluated across four applications with three to ten objectives. Collectively, the four applications capture challenges such as stochastic objectives, severe constraints, nonlinearity and complex Pareto frontiers. The study demonstrates how MOEAs that have shown strong performance on standard test problems can struggle on real-world applications. The benchmarking framework and results have value for enhancing the design and use of MOEAs in real-world applications. Further, the results highlight the need to improve the adaptability and ease-of-use of MOEAs given the often ill-defined nature of real-world problem-solving.

Benchmarking

10.1080/0305215x.2024.2381818

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Efficient Evolutionary Algorithm for Single-Objective Bilevel Optimization

arXiv (Cornell University) (2013)

Ankur Sinha Pekka Malo Kalyanmoy Deb

Bilevel optimization problems are a class of challenging optimization problems, which contain two levels of optimization tasks. In these problems, the optimal solutions to the lower level problem become possible feasible candidates to the upper level problem. Such a requirement makes the optimization problem difficult to solve, and has kept the researchers busy towards devising methodologies, which can efficiently handle the problem. Despite the efforts, there hardly exists any effective methodology, which is capable of handling a complex bilevel problem. In this paper, we introduce bilevel evolutionary algorithm based on quadratic approximations (BLEAQ) of optimal lower level variables with respect to the upper level variables. The approach is capable of handling bilevel problems with different kinds of complexities in relatively smaller number of function evaluations. Ideas from classical optimization have been hybridized with evolutionary methods to generate an efficient optimization algorithm for generic bilevel problems. The efficacy of the algorithm has been shown on two sets of test problems. The first set is a recently proposed SMD test set, which contains problems with controllable complexities, and the second set contains standard test problems collected from the literature. The proposed method has been evaluated against two benchmarks, and the performance gain is observed to be significant.

Bilevel optimization

10.48550/arxiv.1303.3901

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Optimizing Engineering Designs Using a Combined Genetic Search.

ICGA (1997)

Kalyanmoy Deb Mayank Kumar Goyal

In the optimization of engineering designs, traditional search and optimization methods face at least two difficulties: (i) since each is specialized in solving a particular type of problem, one method does not work well on different types of problems (ii) most of them are designed to work on continuous search spaces. Since different optimal engineering design problems give rise to objective and constraint functions of varying degree of nonlinearity and since most engineering design problems involve mixed variables (zero-one, discrete, and continuous), designers often face difficulty in using the traditional methods. In this paper, a combined genetic search technique (GeneAS) is suggested to solve mixed-integer programming problems often encountered in engineering design activities. GeneAS uses a combination of binary-coded and real-coded GAs to handle different types of variables.

Engineering optimization

Continuous variable

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Data mining methods for knowledge discovery in multi-objective optimization: Part A - Survey

Expert Systems with Applications (2016)

Sunith Bandaru Amos H.C. Ng Kalyanmoy Deb

USable

10.1016/j.eswa.2016.10.015

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Citations (164)