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Review Open access Sep 2026

A systematic review of the crested porcupine optimizer: variants and applications

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. · 0 citations
Open access Sep 2026

A Multi-Strategy Enhanced Crested Porcupine Optimizer with Targeted Defense Mechanism Improvement for Global Optimization

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. · 0 citations
Preprint Sep 2026

A hybrid crossover kangaroo escape optimization framework for engineering optimization and UAV path planning

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.

Hang Liu, Hua-Yi Wei · 0 citations
Open access 2026

Enhanced Artificial Protozoa Optimizer via a Multi-Strategy Framework for Engineering Design Problems

: 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. · 0 citations
Open access Aug 2026

An Improved Attraction-Repulsion OptimizationAlgorithm for Global Optimization and EngineeringDesign Problems

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. · 0 citations
Open access Sep 2026

A Tri-Direction Guided Moss Growth Optimizer with Adaptive Differential Evolution for Engineering Design Problems

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...

Chang-Long Pang, Yu-Kun Wang, Wan-Sheng Cheng · 0 citations

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