Titlebook: Metaheuristics; 15th International C Marc Sevaux,Alexandru-Liviu Olteanu,Salma Makboul Conference proceedings 2024 The Editor(s) (if applic

聯(lián)合

Breakout Local Search for?Heaviest Subgraph Problem problem with various practical applications. BLS explores the search space by alternating iteratively between local search phase and dedicated perturbation strategies. Focusing on the perturbation phase, the algorithm determines its jump magnitude and perturbation type according to the search histo

rheumatism

A Biased Random Key Genetic Algorithm for?Solving the?,-Neighbor p-Center Problemdeveloped obtaining competitive solutions for the problem. The objective of the ANPC is to locate . facilities serving demand points and assign a number . of facilities to each demand point. The objective function is evaluated as the maximum distance to the farthest facility assigned to each client,

Exposition

A Continuous-GRASP Random-Key Optimizer continuous GRASP, is an extension of the GRASP metaheuristic for optimization of a general objective function in the continuous unit hypercube. The novel approach extends CGRASP using random keys for encoding solutions of the optimization problem in the unit hypercube and a decoder for evaluating e

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Adaptive Ant Colony Optimization Using Node Clustering with?Simulated AnnealingSP), which is an .-hard combinatorial optimization problems. As the number of cities increases, finding the shortest path in TSP, considering all combinations, becomes challenging. The ant colony optimization?(ACO) has been proposed as a solution to TSP. However, the performance of the ACO heavily d

Isthmus

Job-Shop Scheduling with Robot Synchronization for Transport Operationsure, we assume that a transport operation may involve several robots simultaneously, which requires resource synchronization over time. We formulate this problem as a Mixed Integer Linear Programming (MILP) formulation. Then we propose a GRASP-ELS meta-heuristic and a local search procedure where we

莊嚴(yán)

deface

Investigation of?the?Benefit of?Extracting Patterns from?Local Optima to?Solve a?Bi-objective VRPTWn relevant knowledge that can be used to guide the heuristic towards promising areas. Learning from the structure of solutions is challenging in combinatorial problems. Most of the time, local optima are considered for this task since they tend to contain more relevant structural information. If loc

AV-node

BILK

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