Evolutionary Algorithms for Complex Multi-Objective Optimization Problems

Dynamic Economic Emission Dispatch (DEED) problems present a critical multi-objective optimization challenge in power systems, requiring simultaneous minimization of conflicting objectives of costs and emissions while addressing complex operational constraints. This problem becomes particularly intricate when considering the Valve-Point Effect Cost (VPEC) in generation units, which exhibits non-smooth, non-convex characteristics that significantly complicate solution landscapes. To address these challenges, this talk will discuss a Hybrid Constrained Multi- Objective Algorithm (HCMOA) incorporating three innovative mechanisms. First, a clonal selection mechanism enhances local exploitation through the systematic generation of highquality, non-dominated solutions. Second, a most-crowded neighborhood update strategy combats premature convergence in high-dimensional DEED scenarios while optimizing archive diversity and computational efficiency. Third, a step-by-step repair strategy ensures operational feasibility through systematic output refinement. Comprehensive evaluations on 10-unit and 15-unit systems with varying VPEC configurations demonstrate HCMOA's superiority over ten state-of-the-art algorithms. Experimental results reveal that HCMOA achieves the Pareto-optimal set with superior spread and distribution uniformity compared to existing approaches. These findings collectively establish HCMOA as an effective solution for practical DEED implementations, particularly in scenarios requiring rigorous consideration of non-convex VPEC characteristics.