Learning Sensitive Stigmergic Agents for Solving Complex Problems

Systems composed of several interacting autonomous agents have a huge potential to efficiently address complex real-world problems. Usually agents communicate by directly exchanging information and knowledge about the environment. The aim of the paper is to develop a new computational model that endows agents with a supplementary interaction/search mechanism of stigmergic nature. Multi-agent systems can therefore become powerful techniques for addressing NP-hard combinatorial optimization problems. In the proposed approach, agents are able to indirectly communicate by producing and being influenced by pheromone trails. Each stigmergic agent is characterized by a certain level of sensitivity to the pheromone trails. The non-uniform pheromone sensitivity allows various types of reactions to a changing environment. For efficient search diversification and intensification, agents can learn to modify their sensitivity level according to environment characteristics and previous experience. The resulting system for solving complex problems is called Learning Sensitive Agent System (LSAS). The proposed LSAS model is used for solving several NP-hard problems such as the Asymmetric and Generalized Traveling Salesman Problems. Numerical experiments indicate the robustness and the potential of the new metaheuristic

Big data clustering using grid computing and ant-based algorithm

Big data has the power to dramatically change the way institutes and organizations use their data. Transforming the massive amounts of data into knowledge will leverage the organizations performance to the maximum.Scientific and business organizations would benefit from utilizing big data. However, there are many challenges in dealing with big data such as storage, transfer, management and manipulation of big data.Many techniques are required to explore the hidden pattern inside the big data which have limitations in terms of hardware and software implementation. This paper presents a framework for big data clustering which utilizes grid technology and ant-based algorithm

Ant Colony Optimization With Local Search for Dynamic Traveling Salesman Problems

For a dynamic traveling salesman problem (DTSP), the weights (or traveling times) between two cities (or nodes) may be subject to changes. Ant colony optimization (ACO) algorithms have proved to be powerful methods to tackle such problems due to their adaptation capabilities. It has been shown that the integration of local search operators can significantly improve the performance of ACO. In this paper, a memetic ACO algorithm, where a local search operator (called unstring and string) is integrated into ACO, is proposed to address DTSPs. The best solution from ACO is passed to the local search operator, which removes and inserts cities in such a way that improves the solution quality. The proposed memetic ACO algorithm is designed to address both symmetric and asymmetric DTSPs. The experimental results show the efficiency of the proposed memetic algorithm for addressing DTSPs in comparison with other state-of-the-art algorithms

WoLF Ant

Ant colony optimization (ACO) algorithms can generate quality solutions to combinatorial optimization problems. However, like many stochastic algorithms, the quality of solutions worsen as problem sizes grow. In an effort to increase performance, we added the variable step size off-policy hill-climbing algorithm called PDWoLF (Policy Dynamics Win or Learn Fast) to several ant colony algorithms: Ant System, Ant Colony System, Elitist-Ant System, Rank-based Ant System, and Max-Min Ant System. Easily integrated into each ACO algorithm, the PDWoLF component maintains a set of policies separate from the ant colony\u27s pheromone. Similar to pheromone but with different update rules, the PDWoLF policies provide a second estimation of solution quality and guide the construction of solutions. Experiments on large traveling salesman problems (TSPs) show that incorporating PDWoLF with the aforementioned ACO algorithms that do not make use of local optimizations produces shorter tours than the ACO algorithms alone