scholarly journals Iterated Local Search in Combinatorial Optimization Problem

2019 ◽  
pp. 405-412
Author(s):  
Mehedi Hasan
Keyword(s):  
Combinatorial Optimization ◽  
Local Search ◽  
Optimization Problems ◽  
Optimization Method ◽  
Iterated Local Search ◽  
Military Operations ◽  
Combinatorial Optimization Problems ◽  
Military Operations Research ◽  
The Military ◽  
The Mathematical Model

Iterated local search (ILS) is a very powerful optimization method for continuous-valued numerical optimization. However, ILS has seldom been used to solve combinatorial integer-valued optimization problems. In this paper, the iterated local search (ILS) with random restarts algorithm is applied to solve combinatorial optimization problems, e.g., the classical weapon-target allocation (WTA) problem which arises from the military operations research. The mathematical model of the WTA problem is explained in detail. Then the idea of ILS with random restarts is explained. A comparison of the algorithm with several existing search approaches shows that the ILS outperforms its competitors on the tested WTA problem.

scholarly journals A Multi-Agent Based Optimization Method for Combinatorial Optimization Problems

2018 ◽  
Vol 27 (05) ◽  
pp. 1850021 ◽  
Author(s):  
Ines Sghir ◽  
Ines Ben Jaafar ◽  
Khaled Ghédira
Keyword(s):  
Combinatorial Optimization ◽  
Local Search ◽  
Decision Maker ◽  
Optimization Problems ◽  
Optimization Method ◽  
Quadratic Assignment ◽  
Agent Based ◽  
Combinatorial Optimization Problems ◽  
Multidimensional Knapsack ◽  
Multi Agent

This paper introduces a Multi-Agent based Optimization Method for Combinatorial Optimization Problems named MAOM-COP. In this method, a set of agents are cooperatively interacting to select the appropriate operators of metaheuristics using learning techniques. MAOM-COP is a flexible architecture, whose objective is to produce more generally applicable search methodologies. In this paper, the MAOM-COP explores genetic algorithm and local search metaheuristics. Using these metaheuristics, the decision-maker agent, the intensification agents and the diversification agents are seeking to improve the search. The diversification agents can be divided into the perturbation agent and the crossover agents. The decision-maker agent decides dynamically which agent to activate between intensification agents and crossover agents within reinforcement learning. If the intensification agents are activated, they apply local search algorithms. During their searches, they can exchange information, as they can trigger the perturbation agent. If the crossover agents are activated, they perform recombination operations. We applied the MAOM-COP to the following problems: quadratic assignment, graph coloring, winner determination and multidimensional knapsack. MAOMCOP shows competitive performances compared with the approaches of the literature.

A Research on Flowshop Scheduling Problems With Column Generation

2012 ◽  
Author(s):  
H. Tanohata ◽  
T. Kaihara ◽  
N. Fujii
Keyword(s):  
Combinatorial Optimization ◽  
Local Search ◽  
Column Generation ◽  
Optimization Problems ◽  
New Method ◽  
Duality Gap ◽  
Scheduling Problems ◽  
Flowshop Scheduling ◽  
Feasible Schedule ◽  
Combinatorial Optimization Problems

Column generation is a method to calculate lowerbound for combinatorial optimization problems, although a feasible schedule is generally obtained with the upperbound. Therefore, in this paper, a new method is proposed to solve the flowshop scheduling problems with column generation, which is composed of the local search and duality gap termination condition. The neighborhood of the local search is composed of columns, and the method is applied in column generation to improve the upperbound and lowerbound. The effectiveness of the proposed method is verified by computational experiments.

An Iterated Local Search Algorithm for Fuel Consumption Optimization of Vehicle Routing Problems

2011 ◽  
Vol 148-149 ◽  
pp. 1248-1251
Author(s):  
Xu Dong Wu
Keyword(s):  
Local Search ◽  
Vehicle Routing ◽  
Fuel Consumption ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Iterated Local Search ◽  
Vehicle Routing Problems ◽  
Local Search Algorithm ◽  
Routing Problems ◽  
Combinatorial Optimization Problems

The iterated local search algorithm has been widely used in combinatorial optimization problems. A new fuel consumption objective for the vehicle routing problems was presented in this paper. A fuel consumption modal of the vehicle load is introduced and an improved iterated local search algorithm is used for the problem. An initial solution is generated by the Solomon I1 algorithm, and then the iterated local search algorithm is proposed for the fuel consumption optimization.

An Overview on Set Covering Problems With a Focus on Military Applications

2019 ◽  
pp. 54-66
Author(s):  
Irem Duzdar Argun
Keyword(s):  
Combinatorial Optimization Problem ◽  
Set Covering ◽  
Covering Problem ◽  
Military Operations ◽  
Discrete Location ◽  
Covering Problems ◽  
Set Covering Problems ◽  
Military Operations Research ◽  
The Military ◽  
The Mathematical Model

This chapter deals with the set covering problem, a specific type of a discrete location and representative combinatorial optimization problem, which occupies a huge space in military operations research with its various applications. After presenting a brief literature survey of set covering problems, the author provides a formal description and the mathematical model of the problem. Finally, the key position of those problem types in the military domain and with real-world applications is discussed.

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