Evolutionary algorithms with traditional approaches will not be able to adapt in the case when the environment itself is changing and objectives as well as constraints change over time and need to be retuned continuously. This work proposes Self-Regulated Evolutionary Adaptation (SREA) as a novel metaheuristic approach that automatically adjusts to the environmental changes by changing its required parameters. SREA incorporates a performance feedback loop that measures population diversity and convergence, and automatically self-regulates its selection pressure, mutation rates, and crossover strategies based on these metrics. The algorithm was tested on a set of 462 dynamic multiobjective optimisation problems with various Pareto fronts to simulate complex systems. The entire system was coded in MATLAB using the Global Optimisation Toolbox and problem-specific scripts for execution time. SREA outperforms existing methods across all experiments by identifying the dynamic Pareto-optimal front more accurately and reacting earlier to environmental shifts. SREA is an easy, efficient real-world solution for logistics, resource planning, and engineering design optimisation.
Keywords
Evolutionary AlgorithmsDynamic OptimisationMulti-objective OptimisationComplex Systems
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