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The Exponential Distribution Optimizer (EDO) is a newly developed mathematics-based metaheuristic with a simple structure and high efficiency. However, the EDO faces dilemmas, including poor initial population quality, premature convergence, insufficient population diversity, and low convergence accuracy when addressing complex high-dimensional optimization and cloud computing task scheduling problems. To overcome these drawbacks, this paper proposes an Adaptive Synergistic Exponential Distribution Optimizer (ASEDO) integrated with three collaborative strategies for global optimization and cloud computing task scheduling. First, a Multi-Source Hybrid Perturbation Initialization is designed using first-order differential mutation and high-order Bernstein polynomial perturbation to expand the initial search space and boost population diversity. Second, a Bipolar Adaptive Search Mechanism is presented to enable bidirectional learning from elite and inferior individuals, effectively preventing local optima trapping. Third, an Oscillating Random Mapping Learning Mechanism is introduced to strengthen local search ability and convergence precision via random learning and second-order oscillation mapping. The proposed ASEDO is verified on CEC2022 benchmark functions and cloud computing task scheduling under small-scale, large-scale, and dynamic task scenarios. Ablation experiments and comparison results demonstrate that the synergistic effect of the three strategies significantly improves the performance of EDO. Meanwhile, the ASEDO shows stronger global search capability, higher solution accuracy, and better stability than several state-of-the-art algorithms in both global optimization and cloud task scheduling applications.
Jin et al. (Fri,) studied this question.