Existing ant colony optimization (ACO) for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mechanism is easy to produce the phenomenon of stagnation and precocity. This paper introduces improve...

Ant colony optimization is a technique for optimization that was introduced in the early 1990’s. The inspiring source of ant colony optimization is the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search of approximate solutions to discrete optimization problems, to continuous optimization problems, and to important problems in telecommun...

This paper presents a comparative study of Ant Colony and Genetic Algorithms for VLSI circuit bi-partitioning. Ant colony optimization is an optimization method based on behaviour of social insects [27] whereas Genetic algorithm is an evolutionary optimization technique based on Darwinian Theory of natural evolution and its concept of survival of the fittest [19]. Both the methods are stochasti...

This paper presents a modification of the ant colony optimization algorithm (ACO) intended to introduce psychology factor of emotion into the algorithm. We define only two emotions ants could have, positive and negative, and correspond to two reaction to perception respectively. For avoiding premature convergence allows Emotional Ant Colony Optimization (EACO) to continue search for better even...

Multi Compartment Vehicle Routing Problem is an extension of the classical Capacitated Vehicle Routing Problem where different products are transported together in one vehicle with multiple compartments. Products are stored in different compartments because they cannot be mixed together due to differences in their individual characteristics. The problem is encountered in many industries such as...

For wireless sensor networks, the core issue is to design an energy-efficient network protocol as far as possible. Ant colony algorithm has proved to be effective in prolonging the network’s lifecycle and ensuring the reliability of data transmission. In conditions of network topology’s dynamic changes, we optimized the basic ant colony algorithm and introduced the pheromone broadcasting mechan...

In the paper we present recommendation of student courses using Ant Colony Optimization (ACO). ACO is proved to be effective in solving many optimization problems, here we show that ACO also in the problem of prediction of final grades students receives on completing university courses is able to deliver good solutions. To apply ACO in any recommender system we need special problem representati...

In this study, the novel method to predict chaotic time series is proposed. The method employs the ant colony optimization paradigm to analyze topological structure of the attractor behind the given time series and to single out the typical sequences corresponding to the different part of the attractor. The typical sequences are used to predict the time series values. The method was applied to ...

This paper presents the notion of multi-agent simulation that i s based on the definition of computational agents that represent individual organisms (or groups of organisms) in a one to one correspondence. We discuss the properties of multi-agent simulation. We then present a multiagent simulation system based on the definition of reactive agents whose behavior is governed by the selection of ...

Ant algorithms [18, 14, 19] are a recently developed, population-based approach which has been successfully applied to several NP-hard combinatorial optimization problems [6, 13, 17, 23, 34, 40, 49]. As the name suggests, ant algorithms have been inspired by the behavior of real ant colonies, in particular, by their foraging behavior. One of the main ideas of ant algorithms is the indirect comm...