An Adaptive Genetic Algorithm with Optimal Recombination for Scheduling Problems with Energy Resource

• Published in: Numerical analysis and applications Vol. 18; no. 3; pp. 268 - 282
• Main Author: Sakhno, M. Y.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.09.2025; Springer Nature B.V
• Subjects: Adaptive algorithms; Decoding; Energy consumption; Energy sources; Evolution; Genetic algorithms; Greedy algorithms; Mathematics; Mathematics and Statistics; Multiprocessing; Mutation; Numerical Analysis; Parallel processing; Resource scheduling; Schedules; Scheduling; Software packages; genetic algorithm; adaptive scheme; schedule; parallelizable job; optimized crossover; energy
• ISSN: 1995-4239; 1995-4247
• DOI: 10.1134/S1995423925030073

Some scheduling problems taking into account energy consumption are considered. Such problems arise in multiprocessor computer systems and take into account resource constraints and parallelization capabilities. For these problems, some algorithms of greedy and list types with guaranteed accuracy estimates in the worst case are known. In this paper, we propose an adaptive genetic algorithm with decoding solutions based on the specifics of the problem statements. A peculiarity is that the crossover operator solves a problem of optimal recombination in full and truncated versions. The call of the crossover operators is implemented adaptively. The categorical and numerical parameters are adjusted adaptively by using modern packages. The results of an experimental study show a statistically significant advantage over the known algorithms on a series of problems of different structure.