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A Line Complex-Based Evolutionary Algorithm for Many-Objective Optimization
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DOI：10.1109/JAS.2023.123495.png)
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Abstract
En 中文
In solving many-objective optimization problems (MaOPs), existing nondominated sorting-based multi-objective evolutionary algorithms suffer from the fast loss of selection pressure. Most candidate solutions become nondominated during the evolutionary process, thus leading to the failure of producing off-spring toward Pareto-optimal front with diversity. Can we find a more effective way to select nondominated solutions and resolve this issue? To answer this critical question, this work proposes to evolve solutions through line complex rather than solution points in Euclidean space. First, Plucker coordinates are used to project solution points to line complex composed of position vectors and momentum ones. Besides position vectors of the solution points, momentum vectors are used to extend the comparability of non-dominated solutions and enhance selection pressure. Then, a new distance function designed for high-dimensional space is proposed to replace Euclidean distance as a more effective distance-based estimator. Based on them, a novel many-objective evolutionary algorithm (MaOEA) is proposed by integrating a line complex-based environmental selection strategy into the NSGA-III framework. The proposed algorithm is compared with the state of the art on widely used benchmark problems with up to 15 objectives. Experimental results demonstrate its superior competitiveness in solving MaOPs.
Keywords:
Transfer learning
Evolutionary computation
Euclidean distance
Reinforcement learning
Benchmark testing
Optimization
Convergence
Environmental selection
line complex
many-objective optimization problems (MaOPs)
Plü cker coordinate
Journal
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19.2
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1.4K
Citations:
1.1W