The Crested Porcupine Optimizer, first presented in 2024, is a population-based metaheuristic that has been inspired by the multimodal defense behaviors of crested porcupines. Its unique characteristics in defense-driven movement patterns and explicit repulsion-attraction mechanisms have attracted the attention of many...
Hai-Yu Yu, A. As'arry, Hao-Hao Ma et al.· Applied intelligence (Boston...· 0 citations
Metaheuristic algorithms are widely used to solve complex optimization problems, but the trade-off between exploration and exploitation often limits their performance. The Crested Porcupine Optimizer (CPO) employs four bio-inspired defense mechanisms and achieves competitive performance, but it still tends to converge...
Zhao-Yong Fan, Xi Li, Zhen-Hua Xiao et al.· Biomimetics· 0 citations
This paper develops a hybrid crossover-based optimization framework that enhances population interaction and improves search efficiency, and is applied to UAV path planning, formulated as a constrained optimization problem, demonstrating its effectiveness and scalability in complex engineering scenarios.
: The Artificial Protozoa Optimizer (APO) is a population-based metaheuristic for numerical optimization and engineering design. However, its stochastic initialization and limited local refinement can reduce performance on non-convex, discontinuous, and high-dimensional landscapes. To address these issues, this paper p...
Dingfeng Song, Haibo Wang, Zhiwei Ye et al.· Computers, Materials & C...· 0 citations
Attraction-Repulsion Optimization Algorithm (AROA) is a recently proposed meta-heuristic algorithm known for its simplicity, ease of implementation, and robustness. However, AROA may converge to local optima when applied to complex optimization problems. To address this limitation, we propose an enhanced version called...
Fang Feng, Kuan-Ching Li, Mingjiang Cai et al.· International Journal of Int...· 0 citations
This article proposes an improved Moss Growth Optimization (IMGO) algorithm to address the drawbacks of imbalanced exploration and exploitation and susceptibility to local optima in the original MGO. IMGO integrates three-dimensional guidance, elite guidance, adaptive search, and adaptive adversarial learning to achiev...