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A constrained large-scale lever evolutionary algorithm for white-box problems and its application in spectral-energy efficiency tradeoff of massive MIMO

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MEMETIC COMPUTING 2025年第1期17卷 1-18页

作者： Wang, Qingzhu Li, Tianyang Northeast Elect Power Univ Sch Comp Sci Jilin 132012 Peoples R China

Most existing constrained multi-objective evolutionary algorithms are not so efficient when handling constrained large-scale multi-objective problems (CLSMOPs). To overcome white-box CLSMOPs with definitive objective functions, a spatial-temporal lever evolutionary algorithm (STLEA), consisting of the lever evolutionary algorithm (LEA) and spatial-temporal preference strategy (STPS), is proposed. LEA ditches the thought of the mainstream algorithms for the similar problems, which changes the structure of the large-scale decision space, but handles the large-scale decision space by a certain small-scale decision space. Specifically, inspired by the lever principle, LEA explores the way to pry up the large-scale decision space, as the "load", by the small-scale decision space, as the "force". Meanwhile, LEA rotates the optimizations in between "load" and "force" for dual-balance: balance between objectives and constraints, and balance between convergence and diversity of solutions. STPS dynamically adjusts the proportion of optimizations in "load" and "force". Different from existing preference strategies, which only consider the stage of the evolutionary procedure, STPS considers both stage, related to time, and varying scale of the decision space, related to space, for the comprehensive balance of feasibility, convergence, and diversity of solutions. Eleven representative and state-of-the-art constrained multi-objective evolutionary algorithms have been compared to the proposed STLEA to demonstrate its effectiveness through comparative experiments on through comparative experiments on CLSMOPs with equality and inequality constraints and 1000 decision variables and three typical MaMIMO-LU models with 1024 antennas and 128, 256, and 512 users. Experimental results show that STLEA achieves the best SE-EE tradeoff.

关键词： Massive MIMO Spectral-energy efficiency tradeoff constrained large-scale multi-objective problem multi-objective lever evolutionary algorithm Dynamic balance of objectives and constraint

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A framework for constrained large-scale multi-objective white-box problems based on two-scale optimization through decision transfer

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INFORMATION SCIENCES 2024年 665卷

作者： Wang, Qingzhu Li, Tianyang Meng, Fanqi Li, Bin Northeast Elect Power Univ Sch Comp Sci Jilin 132012 Peoples R China Guangdong Atv Acad Performing Arts Sch Informat Engn Dongguan 523710 Peoples R China

Most existing constrained multi-objective evolutionary algorithms (CMOEAs) are not so efficient when handling constrained large-scale multi-objective problems (CLSMOPs). To overcome whitebox CLSMOPs with definitive objective functions, a two-scale optimization framework based on decision transfer, which integrates dimensionality reduction of large-scale decision variables and constraint handling technology, is proposed. The Lagrange multiplier is first used to construct the two-scale optimization model, which bridges original large-scale decision space of variables and small-scale (2-scale) decision space of objective-constraint parameter. The decision transfer algorithm is then designed to switch between large-scale original decision space and small-scale parametric decision space, while achieving the maximum dimensionality reduction. Finally, the two-scale evolution strategy is proposed for the alternative optimizations in the two decision spaces, which emphasize objectives and constraints, respectively. In summary, the optimization in the large-scale space pushes the population to unconstrained Pareto front (PF), the optimization in the small-scale space helps the population cross the infeasible areas to approach constrained PF, and the offspring generation by Lagrange multiplier is beneficial to both objectives and constraints. Eight representative and state-of-the-art CMOEAs have been embedded into the CLDTEA framework to demonstrate its effectiveness through comparative experiments on CLSMOPs with equality and inequality constraints and 1000 decision variables. Experimental results show that CLDTEA can significantly improve the performance of these basic CMOEAs.

关键词： constrained large-scale multi-objective problem multi-objective evolutionary algorithm Decision transfer Lagrange multiplier

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