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Dual nest pigeon-inspired optimization and its application in engineering constrained optimization problems
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DOI：10.1007/s10586-026-06302-7.png)
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Abstract
En 中文
Engineering constrained optimization problems (e-COPs) present computational challenges for conventional metaheuristic algorithms, particularly regarding population diversity preservation within constrained feasible domains, wherein algorithms frequently encounter premature convergence. This paper introduces a dual-nest pigeon-inspired optimization (DNPIO) algorithm with three key enhancements: a dual-nest operator using an exponential-sorting selection mechanism that balances global and local search strategies; Latin hypercube sampling for population initialization; and a damping boundary condition to explore constraint boundaries. DNPIO was validated using the CEC2017 benchmark dataset. Statistical analysis confirms its superiority, with an average Friedman rank of 1.10 across 29 test functions, with pairwise Wilcoxon tests demonstrating statistically significant advantages in 20 instances. To demonstrate practical applicability, DNPIO was also tested on three complex engineering design problems from CEC2020, achieving optimal solutions for the Welded Beam, Gear Reducer, and Multi-Plate Clutch Brake problems.
Keywords:
Pigeon-inspired optimization
Dual-nest pigeon operator
Latin hypercube sampling
Function optimization
Constrained optimization problems
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