scholarly journals A self-adaptive mechanism using weibull probability distribution to improve metaheuristic algorithms to solve combinatorial optimization problems in dynamic environments

2020 ◽  
Vol 17 (2) ◽  
pp. 975-997
Author(s):  
Cesar J. Montiel Moctezuma ◽  
◽  
Jaime Mora ◽  
Miguel González Mendoza ◽  
Keyword(s):  
Combinatorial Optimization ◽  
Probability Distribution ◽  
Optimization Problems ◽  
Dynamic Environments ◽  
Metaheuristic Algorithms ◽  
Adaptive Mechanism ◽  
Combinatorial Optimization Problems ◽  
Self Adaptive

scholarly journals Nature-Inspired Metaheuristic Techniques for Combinatorial Optimization Problems: Overview and Recent Advances

Mathematics ◽  
2021 ◽  
Vol 9 (20) ◽  
pp. 2633
Author(s):  
Md Ashikur Rahman ◽  
Rajalingam Sokkalingam ◽  
Mahmod Othman ◽  
Kallol Biswas ◽  
Lazim Abdullah ◽  
...  
Keyword(s):  
Combinatorial Optimization ◽  
Industrial Revolution ◽  
Optimization Problems ◽  
Point Of View ◽  
Metaheuristic Algorithms ◽  
Future Research ◽  
Substantial Part ◽  
Combinatorial Optimization Problems ◽  
Hard Problems ◽  
Metaheuristic Techniques

Combinatorial optimization problems are often considered NP-hard problems in the field of decision science and the industrial revolution. As a successful transformation to tackle complex dimensional problems, metaheuristic algorithms have been implemented in a wide area of combinatorial optimization problems. Metaheuristic algorithms have been evolved and modified with respect to the problem nature since it was recommended for the first time. As there is a growing interest in incorporating necessary methods to develop metaheuristics, there is a need to rediscover the recent advancement of metaheuristics in combinatorial optimization. From the authors’ point of view, there is still a lack of comprehensive surveys on current research directions. Therefore, a substantial part of this paper is devoted to analyzing and discussing the modern age metaheuristic algorithms that gained popular use in mostly cited combinatorial optimization problems such as vehicle routing problems, traveling salesman problems, and supply chain network design problems. A survey of seven different metaheuristic algorithms (which are proposed after 2000) for combinatorial optimization problems is carried out in this study, apart from conventional metaheuristics like simulated annealing, particle swarm optimization, and tabu search. These metaheuristics have been filtered through some key factors like easy parameter handling, the scope of hybridization as well as performance efficiency. In this study, a concise description of the framework of the selected algorithm is included. Finally, a technical analysis of the recent trends of implementation is discussed, along with the impacts of algorithm modification on performance, constraint handling strategy, the handling of multi-objective situations using hybridization, and future research opportunities.

Hybrid Self-Adaptive Evolution Strategies Guided by Neighborhood Structures for Combinatorial Optimization Problems

2016 ◽  
Vol 24 (4) ◽  
pp. 637-666 ◽  
Author(s):  
V. N. Coelho ◽  
I. M. Coelho ◽  
M. J. F. Souza ◽  
T. A. Oliveira ◽  
L. P. Cota ◽  
...  
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Evolution Strategy ◽  
Planning Problem ◽  
Search Procedure ◽  
Dynamic Allocation ◽  
Combinatorial Optimization Problems ◽  
Mutation Operators ◽  
Neighborhood Structures ◽  
Self Adaptive

This article presents an Evolution Strategy (ES)--based algorithm, designed to self-adapt its mutation operators, guiding the search into the solution space using a Self-Adaptive Reduced Variable Neighborhood Search procedure. In view of the specific local search operators for each individual, the proposed population-based approach also fits into the context of the Memetic Algorithms. The proposed variant uses the Greedy Randomized Adaptive Search Procedure with different greedy parameters for generating its initial population, providing an interesting exploration–exploitation balance. To validate the proposal, this framework is applied to solve three different [Formula: see text]-Hard combinatorial optimization problems: an Open-Pit-Mining Operational Planning Problem with dynamic allocation of trucks, an Unrelated Parallel Machine Scheduling Problem with Setup Times, and the calibration of a hybrid fuzzy model for Short-Term Load Forecasting. Computational results point out the convergence of the proposed model and highlight its ability in combining the application of move operations from distinct neighborhood structures along the optimization. The results gathered and reported in this article represent a collective evidence of the performance of the method in challenging combinatorial optimization problems from different application domains. The proposed evolution strategy demonstrates an ability of adapting the strength of the mutation disturbance during the generations of its evolution process. The effectiveness of the proposal motivates the application of this novel evolutionary framework for solving other combinatorial optimization problems.

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