A Two-Stage Framework for Cloud Service Combinatorial Optimization: Cognitive-Inspired Co-Evolutionary Dual-Population Search and Contrastive Compensation Ranking

The rapid proliferation of heterogeneous cloud services has made Cloud Service Combinatorial Optimization (CSCO) a fundamental challenge for users and cloud service providers (CSPs) in large-scale computing ecosystems. Its complexity arises from the exponential growth of the decision space due to numerous service providers and tightly coupled resource constraints, making it difficult to efficiently identify high-quality CSP combinations. Moreover, intrinsic conflicts among cost, Quality of Service (QoS), and resource utilization further com plicate the search for balanced solutions. In addition, existing multi-attribute decision-making approaches often fail to capture the compensatory trade-offs between price and QoS, resulting in limited interpretability and weak discrimination among candidate solutions. To address these challenges, we propose a two stage cloud service combinatorial optimization framework that integrates evolutionary search and decision-oriented ranking. In Stage I, a cognitive-inspired co-evolutionary dual-population multi-objective optimization algorithm (CDMOA) is developed to efficiently explore the large CSCO search space. By coordinating a Strategist Population that emphasizes feasibility and cost effectiveness with an Explorer Population that promotes solution diversity, CDMOA generates high-quality and well-distributed Pareto-optimal solutions. In Stage II, a bidirectional price–QoS contrastive compensation (BPQC) method is introduced to rank the obtained Pareto solutions. By integrating global–local contrast analysis with a bounded price–QoS compensation mechanism, BPQC operationalizes relative price–QoS trade-offs and pro vides interpretable rankings for decision support. Extensive experiments demonstrate that the proposed framework achieves competitive optimization performance and more effective decision ranking compared with several state-of-the-art baselines.