Decomposition-Based Multi-Objective Piranha Predation Optimization Algorithm (MOPPOA/D) for Serpentine Belt Drive System Design Problems

The optimization of the serpentine belt drive system (SBDS) is characterized by conflicting objectives, strong nonlinearity and a complicated search space. Traditional multi-objective evolutionary algorithms tend to suffer from insufficient convergence, uneven solution distribution and limited local search capability when tackling problems with complex, degenerated and discontinuous Pareto fronts. To address these drawbacks, this paper proposes a decomposition-based multi-objective piranha predation optimization algorithm (MOPPOA/D). Taking MOEA/D as the basic framework, the proposed algorithm decomposes the multi-objective optimization problem into a set of scalar subproblems with diverse search directions. An evolutionary operator inspired by piranha predation behavior is introduced, which dynamically switches between global exploration and local exploitation according to population satiety. Candidate solutions are generated via straight-line search and spiral search. A total of sixteen three-objective benchmark problems from the DTLZ and WFG test suites are selected to compare MOPPOA/D with NSGA-II, MOEA/D, MOEA/D-DQN and MOEA/D-AWA. Experimental results reveal that MOPPOA/D achieves the optimal mean HV and IGD values on 11 and 8 test problems, respectively. Furthermore, MOPPOA/D is applied to parameter optimization of the tensioner in the SBDS. Engineering calculations show that the maximum ratio of dynamic tension amplitude to installation tension for belt spans decreases by 18.63%, while the maximum average pulley slip rate declines by 9.07%. Despite a 5.88% increase in the maximum tensioner swing amplitude, the value is still within the allowable engineering range. The results verify the effectiveness and engineering application potential of MOPPOA/D on complex multi-objective benchmarks and the design optimization of the SBDS.