A multi-agent approach to solving dynamic Traveling Salesman Problem (TSP) is presented. In the dynamic version of TSP cities can be dynamically added or removed. Proposed multi-agent approach is based on the Sensitive Stigmergic Agent System model refined with new type of messages between agents. The agents send messages every time a change occurs, for instance, when an agent observes that a c...

This study proposes an Ant Colony Optimization using Genetic Information (GIACO). The GIACO algorithm combines Ant Colony Optimization (ACO) with Genetic Algorithm (GA). GIACO searches solutions by using the pheromone of ACO and the genetic information of GA. In addition, two kinds of ants coexist: intelligent ant and dull ant. The dull ant is caused by the mutation and cannot trail the pheromo...

Ensemble-based learning is a very promising option to reach a robust partition. Due to covering the faults of each other, the classifiers existing in the ensemble can do the classification task jointly more reliable than each of them. Generating a set of primary partitions that are different from each other, and then aggregation the partitions via a consensus function to generate the final part...

In this paper we focus on finding high quality solutions for the problem of maximum partitioning of graphs with supply and demand (MPGSD). There is a growing interest for the MPGSD due to its close connection to problems appearing in the field of electrical distribution systems, especially for the optimization of self-adequacy of interconnected microgrids. We propose an ant colony optimization ...

The goal of grid computing is to provide powerful computing for complex scientific problems by utilizing and sharing large scale resources available in the grid. Efficient scheduling algorithms are needed to allocate suitable resources for each submitted task. So scheduling is one of the most important issues for achieving high performance computing in grid. This paper addresses an approach for...

Large-scale problems are nonlinear problems that need metaheuristics, or global optimization algorithms. This paper reviews nature-inspired metaheuristics, then it introduces a framework named Competitive Ant Colony Optimization inspired by the chemical communications among insects. Then a case study is presented to investigate the proposed framework for large-scale global optimization.

Emergency logistics routing problem, which is premise with the time requirements for emergency logistics and aims at maximum saving delivery time for relief supplies, is a reasonable arrangement of vehicles to run routes. According to the characteristics of time which emergency logistics emphasis on, the delivery route optimization model which the number of delivery vehicles less than demand ar...

One of the new major directions in research on web-based educational systems is the notion of adaptability: the educational system adapts itself to the learning profile, preferences and ability of the student. In this paper, we look into the issues of providing adaptability with respect to learning pathways. We explore the state of the art with respective to deriving the most apt learning pathw...

Ant Colony Optimization (ACO) a natureinspired metaheuristic algorithm has been successfully applied in the traveling salesman problem (TSP) and a variety of combinatorial problems. ACO algorithms have been modified in recent years to improve the performance of the first algorithm, posed by Dorigo. In this paper we compare different ACO algorithms and combine them in order to collect their adva...

We present a new mechanism to introduce diversity into two multiobjective approaches based on ant colony optimisation and randomised greedy algorithms to solve a more realistic extension of a classical industrial problem: time and space assembly line balancing. Promising results are shown after applying the designed constructive metaheuristics to ten real-like problem instances.