A genetic local search algorithm for solving symmetric and asymmetric traveling salesman problems

Design and analysis of stochastic local search for the multiobjective traveling salesman problem

“…Then, it iterates r times over the following two steps. First, it chooses two among the best m solutions in A with respect to a weighted sum based on randomly generated weights; these two solutions are then recombined by the distance-preserving crossover [46]. Next, it applies a different iterative improvement algorithm based on the 2-exchange neighborhood to the new recombined solution using the same weighted sum scalarization; the resulting local optimum s * is added to archive CS if it is better than the worst solution among the m best solutions according to the weight vector considered; finally s * is added to archive A, if no solution dominates it and the archive A is updated.…”

Heuristic algorithms and learning techniques: applications to the graph coloring problem

“…Similar techniques can be found for other combinatorial problems, e.g. see the distance preserving crossover for the traveling salesman problem [Freisleben and Merz, 1996]. However, our crossover is not intended to ensure equal distances between the offspring solution and parents; this issue is indirectly addressed only by the rejection and replacement strategies.…”

S-MINE Algorithm for the TSP

“…5장은 실험 결과를 고찰하여, 향후 발전 방향과 과제를 제시하였다. GA는 인공유전 시스템으로 자연세계의 진화과정에 기초 한 계산모델로 John Holland에 의해서 1975년 개발된 전역 적인 최적화 알고리즘이다 [3]. GA는 풀고자 하는 문제에 대 한 가능한 해들을 정해진 형태의 자료구조로 표현한 다음 이들을 점차적으로 변형함으로써 점점 더 좋은 해들을 만들 어 낸다 [15].…”

“…따라서 TSP에 적합한 제약조건을 만족하는 연산자를 사용해야 한 다 [15]. 이와 같이 제약조건을 만족하는 알려진 GA 연산자 로는 REVERSE, TRANSPORT, SWEEP, ORDER CROSSOVER, CYCLE CROSSOVER, PARTIALLY MATCHED CROSSOVER, EDGE RECOMBINATION 등 의 연산자가 있다 [3][16] [15].…”