Jul 2026· International Conference on Control, Decision and Information Technologies· pp. 2193-2198· 0 citations· 17 references
Abstract
The Dynamic Multi-Objective Bin-Packing Problem (DMOBPP) presents significant challenges in simultaneously optimizing bin usage (z1), thermal heat (z2), and delivery time (z3). This paper proposes AMSS-PATH, a hybrid metaheuristic integrating Strategic Path Relinking into an Adaptive Multi-Objective Scatter Search (AMSS) framework with three specialized operators (Φ1,Φ2,Φ3), each targeting a distinct objective. By exploring elite-solution trajectories, the method densifies the Pareto frontier through targeted intensification at every intermediate point. Experimental results on toy and full-scale instances yield a Net Front Contribution (NFC) of 100%, a Binary Conflict Index (BCI) of ≈ 0, and a 4.8% hypervolume gain over base AMSS, significantly outperforming GAMMA-PC, MOMA, and NSGA-II. A Wilcoxon signed-rank test (p ≈ 0.031) confirms statistical significance.
Metaheuristics require sustained global search without sacrificing local refinement, yet many variable-structure methods change operators through one-way iteration schedules. We introduce the Weather State Ants Optimizer (WSAO), in which a discrete-time Markov chain recurrently selects one of three population updates....
The three-dimensional single bin packing problem with categorical compatibility constraints (3D-SBPP-CC) extends the classical NP-hard packing problem by adding pairwise incompatibility rules over item categories. Although such constraints arise in hazardous materials logistics, no prior study has provided a complete m...
Yi-Nan Jin, Zhao-Long Xuan, Tian-Peng Li et al.· Algorithms· 0 citations
This work proves that a widely-studied MOEA, GSEMO, using unit-step mutation can be trapped in local optimal regions and fail to identify the Pareto front, and demonstrates the extendability of the proposed benchmark to more complex landscapes with numerous local optima, resembling well-established problems in the fiel...
Yue-Tong Sun, Zeqiong Lv, Sheng-Jie Ren et al.· Proceedings of the Thirty-Fi...· 0 citations
Minimising the makespan for large job sets is the goal of the algorithmically intensive NP-hard permutation flow shop scheduling problem (PFSP), which is crucial for real-world applications. Originally designed for continuous optimisation, the snake optimiser is a recently discovered swarm-based metaheuristic that may...
Hassan Almazini, Salah Mortada, H. Almazini· Applied Computer Science· 0 citations
Cuckoo Search Algorithm (CSA) has emerged as a powerful bio-inspired metaheuristic, yet its reliance on random-walk search often leads to slow convergence and susceptibility to local optima. This study introduces an Adaptive Elite Cuckoo Search Algorithm (AECSA), designed to enhance both convergence speed and solution...
P. Ong, Jia-Hang Wu· Journal of Computing Researc...· 0 citations
The reviewed evidence indicates that metaheuristic approaches can provide flexible search mechanisms in computationally challenging settings, while learning-based models may support rapid inference, prediction, initialization, or search guidance when appropriate training data and generalization conditions are available...
S. El Kafhali, Mohamed Abid, Mohamed Hanini· Mathematical and Computation...· 1 citation
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