IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems

By Yanan Sun, Gary G. Yen, Zhang Yi. 2018

472 citations recorded by the source corpus.

Inverted generational distance (IGD) has been widely considered as a reliable performance indicator to concurrently quantify the convergence and diversity of multiobjective and many-objective evolutionary algorithms. In this paper, an IGD indicator-based evolutionary algorithm for solving many-objective optimization problems (MaOPs) has been proposed. Specifically, the IGD indicator is employed in each generation to select the solutions with favorable convergence and diversity. In addition, a computationally efficient dominance comparison method is designed to assign the rank values of solutions along with three newly proposed proximity distance assignments. Based on these two designs, the solutions are selected from a global view by linear assignment mechanism to concern the convergence and diversity simultaneously. In order to facilitate the accuracy of the sampled reference points for the calculation of IGD indicator, we also propose an efficient decomposition-based nadir point estimation method for constructing the Utopian Pareto front (PF) which is regarded as the best approximate PF for real-world MaOPs at the early stage of the evolution. To evaluate the performance, a series of experiments is performed on the proposed algorithm against a group of selected state-of-the-art many-objective optimization algorithms over optimization problems with 8-, 15-, and 20-objective. Experimental results measured by the chosen performance metrics indicate that the proposed algorithm is very competitive in addressing MaOPs.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Industrial and Manufacturing Engineering

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Citations in the corpus

Automatically Designing CNN Architectures Using Genetic Algorithm for Image Classification (2018, background): “…include genetic algorithms (GAs) [23], genetic programming [24], evolutionary strategy [25], etc., among which GAs are the most popular one mainly because of their theoretical evidences [26] and promising performance in solving different optimization problems [27], [28], [29], [30], [31].”
A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization (2019, background): “[25] proposed an IGD-based MaOEA (MaOEAIGD) that showed promising performance in solving MaOPs.”
Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification (2018, methodology): “…evolutionary algorithms include genetic algorithms (GAs) [23], genetic programming [24], evolutionary strategy [25], etc., among which GAs are the most popular one mainly because of their theoretical evidences [26] and promising performance in solving different optimization problems [27]–[31].”
Completely Automated CNN Architecture Design Based on Blocks (2020, background): “Because of the nature of gradient-free and insensitiveness to the local minimum, GAs are preferred, especially in engineering ﬁelds where the optimization problems are commonly nonconvex and nondifferentiable [33], [34].”
A Survey on Evolutionary Neural Architecture Search (2020, unknown): “Owing to the promising characteristics of the EC methods in insensitiveness to the local minima and no requirement to gradient information, EC has been widely applied to solve complex non-convex optimization problems [17], even when the mathematical form of the objective function is not available [1”