Comprehensive benefit evaluation of mineral resources development based on dual population constrained multi-objective evolutionary algorithm - Yue, Xuezhi; Cheng, Yating; Kang, Lanlan; Peng, Hu; Zeng, Yuan (Applied soft computing, 2025)
With the increasing global demand for mineral resources, the comprehensive benefit evaluation of mineral resources development is playing an increasingly important role in the field of mineral resources development. However, there are few attempts to use multi-objective evolutionary algorithms (MOEAs) to study the comprehensive benefit evaluation problem of mineral resources development. To effectively solve this problem, this study established a multi-objective optimization model for comprehensive benefit evaluation of mineral resources development and proposed a dual-population constrained multi-objective evolutionary algorithm (CMOEA). The algorithm uses two populations to organically combine the efficient convergence of the non-dominated sorting genetic algorithm II (NSGA-II) with the advantage of the adaptive geometry estimation based multi-objective evolutionary algorithm (AGE-MOEA) in estimating the shape of the Pareto front, named co-evolution based on non-dominated sorting genetic algorithm and adaptive geometry estimation (C-NSGA-AGE). 3 test suites are used to verify the performance of the proposed algorithm. Experimental results show that compared with competing algorithms, C-NSGA-AGE has strong competitiveness in most test functions. The proposed method is used to solve the model, and five optimal comprehensive benefit evaluation schemes are given for different objectives. Among them, the maximum benefit is obtained when each objective is developed in a balanced way, which reaches 0.837181. •Mathematical model for comprehensive benefit evaluation is formulated.•A dual-population constrained multi-objective evolutionary algorithm is proposed.•C-NSGA-AGE algorithm demonstrates superior performance compared to advanced CMOEAs.