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Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Data structures for traveling salesmen
SODA '93 Selected papers from the fourth annual ACM SIAM symposium on Discrete algorithms
ACM Computing Surveys (CSUR)
Future Generation Computer Systems
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Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Fitness landscapes and evolvability
Evolutionary Computation
Learning at the Knowledge Level
Machine Learning
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator
Proceedings of the 3rd International Conference on Genetic Algorithms
The Traveling Salesrep Problem, Edge Assembly Crossover, and 2-opt
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Inver-over Operator for the TSP
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Deterministic Multi-step Crossover Fusion: A Handy Crossover Composition for GAs
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A Multilevel Approach to the Travelling Salesman Problem
Operations Research
Chained Lin-Kernighan for Large Traveling Salesman Problems
INFORMS Journal on Computing
Tour Merging via Branch-Decomposition
INFORMS Journal on Computing
Take a walk and cluster genes: a TSP-based approach to optimal rearrangement clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Adopting ontology to facilitate knowledge sharing
Communications of the ACM - Bioinformatics
A Compact Multiagent System based on Autonomy Oriented Computing
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Human Problem Solving
How autonomy oriented computing (AOC) tackles a computationally hard optimization problem
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Toward nature-inspired computing
Communications of the ACM
Particle swarm optimization-based algorithms for TSP and generalized TSP
Information Processing Letters
Increasing Population Diversity Through Cultural Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Combining multiple heuristics online
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
The backbone of the travelling salesperson
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A novel local search algorithm for the traveling salesman problem that exploits backbones
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Reducing the size of traveling salesman problem instances by fixing edges
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
A hybrid heuristic for the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
MAGMA: a multiagent architecture for metaheuristics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An evolutionary algorithm for large traveling salesman problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Implementation of an Effective Hybrid GA for Large-Scale Traveling Salesman Problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An Autonomy-Oriented Paradigm for Self-Organized Computing
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Use of MaSE methodology for designing a swarm-based multi-agent system
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Knowledge integration and management in autonomous systems
Fundamenta Informaticae - Methodologies for Intelligent Systems
Parallelized genetic ant colony systems for solving the traveling salesman problem
Expert Systems with Applications: An International Journal
Complex Task Allocation in Mobile Surveillance Systems
Journal of Intelligent and Robotic Systems
Applications of agent-based models for optimization problems: A literature review
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A distributed agent-based approach for simulation-based optimization
Advanced Engineering Informatics
A novel bio-inspired approach based on the behavior of mosquitoes
Information Sciences: an International Journal
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The multiagent optimization system (MAOS) is a nature-inspired method, which supports cooperative search by the self-organization of a group of compact agents situated in an environment with certain sharing public knowledge. Moreover, each agent in MAOS is an autonomous entity with personal declarative memory and behavioral components. In this paper, MAOS is refined for solving the traveling salesman problem (TSP), which is a classic hard computational problem. Based on a simplified MAOS version, in which each agent manipulates on extremely limited declarative knowledge, some simple and efficient components for solving TSP, including two improving heuristics based on a generalized edge assembly recombination, are implemented. Compared with metaheuristics in adaptive memory programming, MAOS is particularly suitable for supporting cooperative search. The experimental results on two TSP benchmark data sets show that MAOS is competitive as compared with some state-of-the-art algorithms, including the Lin-Kernighan-Helsgaun, IBGLK, PHGA, etc., although MAOS does not use any explicit local search during the runtime. The contributions of MAOS components are investigated. It indicates that certain clues can be positive for making suitable selections before time-consuming computation. More importantly, it shows that the cooperative search of agents can achieve an over-all good performance with a macro rule in the switch mode, which deploys certain alternate search rules with the offline performance in negative correlations. Using simple alternate rules may prevent the high difficulty of seeking an omnipotent rule that is efficient for a large data set.