Guided local search

Guided local search is a heuristic algorithm that is used in metaheuristic algorithms such as tabu search and simulated annealing. It involves modifying the objective function dynamically during the search process to guide the search towards promising regions of the search space. The algorithm is used to improve the initial solution by applying improvement algorithms such as guided local search, simulated annealing, and tabu search.From: Global Logistics Management [2019], Advances in Swarm Intelligence for Optimizing Problems in Computer Science [2018], A two-stage metaheuristic algorithm for the dynamic vehicle routing problem in Industry 4.0 approach [2021]

Introduction to Swarm Intelligence

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Published in Anand Nayyar, Dac-Nhuong Le, Nhu Gia Nguyen, Advances in Swarm Intelligence for Optimizing Problems in Computer Science, 2018

Anand Nayyar, Nhu Gia Nguyen

Algorithms with dynamic objective function like guided local search can modify the function during the search process. One versus multiple neighbourhood structures

A two-stage metaheuristic algorithm for the dynamic vehicle routing problem in Industry 4.0 approach

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Published in Journal of Management Analytics, 2021

Maryam Abdirad, Krishna Krishnan, Deepak Gupta

In this paper, a two-stage algorithm was proposed for solving a DVRP. In the first stage of the proposed methodology, construction algorithms such as Savings algorithm, Path cheapest Arc algorithm, and Global Cheapest Arc are used to construct the initial route. In the second stage, some improvement algorithms such as Guided Local Search, Simulated Annealing, and Tabu Search algorithms are applied to improve the initial route. To verify the proposed methodology, the new solutions are compared to existing metaheuristics in the same category. The lowest transportation cost from all these combinations is selected as the best answer to this problem. The two-stage algorithm was tested on multiple case studies to evaluate its effectiveness. As explained in the results section, the proposed two-stage algorithm results in lower transportation costs.

Guided local search

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Introduction to Swarm Intelligence

A two-stage metaheuristic algorithm for the dynamic vehicle routing problem in Industry 4.0 approach