The fleet assignment problem: solving a large-scale integer program
Mathematical Programming: Series A and B
Competition-based neural network for the multiple travelling salesmen problem with minmax objective
Computers and Operations Research - Special issue on the traveling salesman problem
Transformation of Multisalesman Problem to the Standard Traveling Salesman Problem
Journal of the ACM (JACM)
MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows
New ideas in optimization
Distributed evolutionary optimization, in Manifold: Rosenbrock's function case study
Information Sciences: an International Journal - Special issue on frontiers in evolutionary algorithms
Multiagent learning using a variable learning rate
Artificial Intelligence
A heuristic approach to the overnight security service problem
Computers and Operations Research
D-Ants: savings based ants divide and conquer the vehicle routing problem
Computers and Operations Research
A comparative study of probability collectives based multi-agent systems and genetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Cross-Layer Optimization Framework for Multicast in Multi-hop Wireless Networks
WICON '05 Proceedings of the First International Conference on Wireless Internet
An Ant Colony Optimization Algorithm for Multiple Travelling Salesman Problem
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1
Priority-based assignment and routing of a fleet of unmanned combat aerial vehicles
Computers and Operations Research
EURASIP Journal on Wireless Communications and Networking
Very Large-Scale Neighborhood Search for the Quadratic Assignment Problem
INFORMS Journal on Computing
Rational and convergent learning in stochastic games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Min-Cost Selfish Multicast With Network Coding
IEEE Transactions on Information Theory
A new evolutionary algorithm using shadow price guided operators
Applied Soft Computing
A revision algorithm for invalid encodings in concurrent formation of overlapping coalitions
Applied Soft Computing
Probability collectives multi-agent systems: a study of robustness in search
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
A study of probability collectives multi-agent systems on optimization and robustness
Transactions on computational collective intelligence IV
Combinatorial optimization in Biology using Probability Collectives Multi-agent Systems
Expert Systems with Applications: An International Journal
A probability collectives approach with a feasibility-based rule for constrained optimization
Applied Computational Intelligence and Soft Computing
A distributed agent-based approach for simulation-based optimization
Advanced Engineering Informatics
A classification scheme for agent based approaches to dynamic optimization
Artificial Intelligence Review
A Market-based Solution to the Multiple Traveling Salesmen Problem
Journal of Intelligent and Robotic Systems
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Complex systems generally have many components. It is not possible to understand such complex systems only by knowing the individual components and their behavior. This is because any move by a component affects the further decisions/moves by other components and so on. In a complex system, as the number of components grows, complexity also grows exponentially, making the entire system to be seen as a collection of subsystems or a Multi-Agent System (MAS). The major challenge is to make these agents work in a coordinated way, optimizing their local utilities and contributing the maximum towards optimization of the global objective. This paper discusses the theory of Collective Intelligence (COIN) using the modified version of Probability Collectives (PC) to achieve the global goal. The paper successfully demonstrated this approach by optimizing the Rosenbrock function in which the coupled variables are seen as autonomous agents working collectively to achieve the function optimum. To demonstrate the PC approach on combinatorial optimization problems, two test cases of the Multi-Depot Multiple Traveling Salesmen Problem (MDMTSP) with 3 depots, 3 vehicles and 15 nodes are solved. In these cases, the vehicles are considered as autonomous agents collectively searching the minimum cost path. PC is successfully accompanied with insertion, elimination and swapping heuristic techniques. The optimum results to the Rosenbrock function and both the MDMTSP test cases are obtained at reasonable computational costs.