Multi-center variable-scale search algorithm for combinatorial optimization problems with the multimodal property

2019 ◽  
Vol 84 ◽  
pp. 105726
Author(s):  
Hui Lu ◽  
Rongrong Zhou ◽  
Shi Cheng ◽  
Yuhui Shi
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Combinatorial Optimization Problems

Study on Free Search for Combinatorial Optimization Problem

2013 ◽  
Vol 411-414 ◽  
pp. 1904-1910
Author(s):  
Kai Zhong Jiang ◽  
Tian Bo Wang ◽  
Zhong Tuan Zheng ◽  
Yu Zhou
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Process ◽  
Standard Data ◽  
Combinatorial Optimization Problems ◽  
Free Search

An algorithm based on free search is proposed for the combinatorial optimization problems. In this algorithm, a feasible solution is converted into a full permutation of all the elements and a transformation of one solution into another solution can be interpreted the transformation of one permutation into another permutation. Then, the algorithm is combined with intersection elimination. The discrete free search algorithm greatly improves the convergence rate of the search process and enhances the quality of the results. The experiment results on TSP standard data show that the performance of the proposed algorithm is increased by about 2.7% than that of the genetic algorithm.

TCGD: A Time-Constrained Approximate Guided Depth-First Search Algorithm

1997 ◽  
Vol 06 (02) ◽  
pp. 255-271 ◽  
Author(s):  
Benjamin W. Wah ◽  
Lon-Chan Chu
Keyword(s):  
Combinatorial Optimization ◽  
Polynomial Time ◽  
Execution Time ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Fixed Time ◽  
Depth First Search ◽  
Combinatorial Optimization Problems ◽  
Feasible Solutions

In this paper, we develop TCGD, a problem-independent, time-constrained, approximate guided depth-first search (GDFS) algorithm. The algorithm is designed to achieve the best ascertained approximation degree under a fixed time constraint. We consider only searches with finite search space and admissible heuristic functions. We study NP-hard combinatorial optimization problems with polynomial-time computable feasible solutions. For the problems studied, we observe that the execution time increases exponentially as approximation degree decreases, although anomalies may happen. The algorithms we study are evaluated by simulations using the symmetric traveling-salesperson problem.

A quantum-inspired Tabu search algorithm for solving combinatorial optimization problems

2013 ◽  
Vol 18 (9) ◽  
pp. 1771-1781 ◽  
Author(s):  
Hua-Pei Chiang ◽  
Yao-Hsin Chou ◽  
Chia-Hui Chiu ◽  
Shu-Yu Kuo ◽  
Yueh-Min Huang
Keyword(s):  
Combinatorial Optimization ◽  
Tabu Search ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Combinatorial Optimization Problems

A Binary Cuckoo Search Algorithm for Graph Coloring Problem

2014 ◽  
Vol 5 (3) ◽  
pp. 42-56 ◽  
Author(s):  
Halima Djelloul ◽  
Abdesslem Layeb ◽  
Salim Chikhi
Keyword(s):  
Combinatorial Optimization ◽  
Graph Coloring ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Coloring Problem ◽  
Combinatorial Optimization Problems ◽  
Hybrid Approaches ◽  
Graph Coloring Problem

The Graph Coloring Problem (GCP) is one of the most interesting, studied, and difficult combinatorial optimization problems. That is why several approaches were developed for solving this problem, including exact approaches, heuristic approaches, metaheuristics, and hybrid approaches. This paper tries to solve the graph coloring problem using a discrete binary version of cuckoo search algorithm. To show the feasibility and the effectiveness of the algorithm, it has used the standard DIMACS benchmark, and the obtained results are very encouraging.

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