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Data · dataset · 2022

SUPPLEMENTARY DATA OF THE PAPER: A Distributed Bi-behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization Problems

Listed in Teesside University Research Data Repository

This study proposes a Distributed Bi-behaviors Crow Search Algorithm (DB-CSA) with two different mechanisms, one corresponding to the search behavior and another to the exploitative behavior with a dynamic switch mechanism.

Description

The bi-behaviors CSA chasing profile is defined based on a large Gaussian-like Beta-1 function which ensures diversity enhancement, while the narrow Gaussian Beta-2 function is used to improve the solution tuning and convergence behavior.

Two variants of the proposed DB-CSA approach are developed, the first variant is considered for solving a set of MaOPs with 2, 3, 5, 7, 8, 10,15 objectives and the second aims to solve several types of DMOPs with different time-varying Pareto optimal set and Pareto optimal front. The second variant of DB-CSA algorithm (DB-CSA-II) is proposed for solving DMOPs including a dynamic optimization process to detect and react effectively to the dynamic change.

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The Inverted General Distance, the Mean Inverted General Distance and the Hypervolume Difference are the main measurement metrics are used to compare the DB-CSA approach to the state-of-the-art MOEAs. All quantitative results are analyzed using the non-parametric Wilcoxon signed rank test with 0.05 significance level which proving the efficiency of the proposed method for solving 44 test beds (21 DMOPs and 23 MaOPS).

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Teesside University Research Data Repositoryoai:data.mendeley.com/hydzpsv4tp.112 d agoJSON v1
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