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We propose a new genetic algorithm with optimal recombination for the asymmetric instances of travelling salesman problem. The algorithm incorporates several new features that contribute to its effectiveness: (i) Optimal recombination…

神经与进化计算 · 计算机科学 2017-12-20 A. V. Eremeev , Yu. V. Kovalenko

This research conducts a comparative analysis of four Ant Colony Optimization (ACO) variants -- Ant System (AS), Rank-Based Ant System (ASRank), Max-Min Ant System (MMAS), and Ant Colony System (ACS) -- for solving the Traveling Salesman…

神经与进化计算 · 计算机科学 2024-05-27 Ahmed Mohamed Abdelmoaty , Ibrahim Ihab Ibrahim

In this paper, we propose a two-stage optimization strategy for solving the Large-scale Traveling Salesman Problems (LSTSPs) named CCPNRL-GA. First, we hypothesize that the participation of a well-performed individual as an elite can…

神经与进化计算 · 计算机科学 2022-09-28 Rui Zhong , Enzhi Zhang , Masaharu Munetomo

Ant Colony Optimization (ACO) has time complexity O(t*m*N*N), and its typical application is to solve Traveling Salesman Problem (TSP), where t, m, and N denotes the iteration number, number of ants, number of cities respectively. Cutting…

神经与进化计算 · 计算机科学 2009-07-07 Chao-Yang Pang , Wei Hu , Xia Li , Be-Qiong Hu

The Generalized Traveling Salesman Problem (GTSP) is a well-known combinatorial optimization problem with a host of applications. It is an extension of the Traveling Salesman Problem (TSP) where the set of cities is partitioned into…

数据结构与算法 · 计算机科学 2012-02-15 Daniel Karapetyan , Gregory Gutin

The world is connected through the Internet. As the abundance of Internet users connected into the Web and the popularity of cloud computing research, the need of Artificial Intelligence (AI) is demanding. In this research, Genetic…

神经与进化计算 · 计算机科学 2018-02-12 Aryo Pinandito , Novanto Yudistira , Fajar Pradana

The Travelling Salesman Problem (TSP) is one of the most popular Combinatorial Optimization Problem. It is well solicited for the large variety of applications that it can solve, but also for its difficulty to find optimal solutions. One of…

神经与进化计算 · 计算机科学 2020-06-30 Mehdi El Krari , Belaïd Ahiod

This paper presents a novel approach to solving the Flying Sidekick Travelling Salesman Problem (FSTSP) using a state-of-the-art self-adaptive genetic algorithm. The Flying Sidekick Travelling Salesman Problem is a combinatorial…

神经与进化计算 · 计算机科学 2023-10-24 Ted Pilcher

With applications to many disciplines, the traveling salesman problem (TSP) is a classical computer science optimization problem with applications to industrial engineering, theoretical computer science, bioinformatics, and several other…

人工智能 · 计算机科学 2017-05-26 Yihui He , Ming Xiang

Solutions to the Traveling Salesperson Problem (TSP) have practical applications to processes in transportation, logistics, and automation, yet must be computed with minimal delay to satisfy the real-time nature of the underlying tasks.…

机器学习 · 计算机科学 2022-04-06 Benjamin Hudson , Qingbiao Li , Matthew Malencia , Amanda Prorok

The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. In the dynamic traveling salesman problem the distances between cities as travel times are no longer fixed. The…

人工智能 · 计算机科学 2020-07-28 Camelia-M. Pintea , Gloria Cerasela Crisan , Mihai Manea

In this paper we discuss the application of Artificial Intelligence (AI) to the exemplary industrial use case of the two-dimensional commissioning problem in a high-bay storage, which essentially can be phrased as an instance of Traveling…

神经与进化计算 · 计算机科学 2024-04-16 Stefan Wintersteller , Martin Uray , Michael Lehenauer , Stefan Huber

Traveling Salesman Problem (TSP) is one of the most common studied problems in combinatorial optimization. Given the list of cities and distances between them, the problem is to find the shortest tour possible which visits all the cities in…

分布式、并行与集群计算 · 计算机科学 2014-01-27 Harun Rasit Er , Nadia Erdogan

The genetic algorithm (GA) is an optimization and search technique based on the principles of genetics and natural selection. A GA allows a population composed of many individuals to evolve under specified selection rules to a state that…

神经与进化计算 · 计算机科学 2016-08-14 Yılmaz Kaya , Murat Uyar , Ramazan Tek\D{j}n

Ant Colony Optimisation (ACO) is a well known metaheuristic that has proven successful at solving Travelling Salesman Problems (TSP). However, ACO suffers from two issues; the first is that the technique has significant memory requirements…

神经与进化计算 · 计算机科学 2017-09-12 Darren M. Chitty

Evolutionary Multi-Objective Optimization is becoming a hot research area and quite a few papers regarding these algorithms have been published. However the role of local search techniques has not been expanded adequately. This paper…

人工智能 · 计算机科学 2011-09-07 Rohan Agrawal

This article presents a new algorithm which is a modified version of the elite ant system (EAS) algorithm. The new version utilizes an effective criterion for escaping from the local optimum points. In contrast to the classical EAC…

人工智能 · 计算机科学 2012-02-08 Majid Yousefikhoshbakht , Farzad Didehvar , Farhad Rahmati

This paper addresses the Traveling Salesman Problem with Drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or…

人工智能 · 计算机科学 2019-11-20 Quang Minh Ha , Yves Deville , Quang Dung Pham , Minh Hoàng Hà

Ant-based algorithms are successful tools for solving complex problems. One of these problems is the Linear Ordering Problem (LOP). The paper shows new results on some LOP instances, using Ant Colony System (ACS) and the Step-Back Sensitive…

人工智能 · 计算机科学 2012-08-28 Camelia-M. Pintea , Camelia Chira , D. Dumitrescu

Ant colony system (ACS) is a promising approach which has been widely used in problems such as Travelling Salesman Problems (TSP), Job shop scheduling problems (JSP) and Quadratic Assignment problems (QAP). In its original implementation,…

人工智能 · 计算机科学 2016-10-27 M. K. A. Ariyaratne , T. G. I. Fernando , S. Weerakoon