Ant colony optimization (ACO) was inspired by the observation of natural behavior of real ants’ pheromone trail formation and foraging. Ant colony optimization ismore suitable for combinatorial optimization problems. ACO is successfully applied to the traveling salesman problem. Multistage decision making of ACO gives an edge over other conventional methods. This paper proposes evolving ant col...

Over the years, several variations of the dynamic vehicle routing problem (DVRP) have been considered due to its similarities with many real-world applications. Several methods have been applied to address DVRPs, in which ant colony optimization (ACO) has shown promising results due to its adaptation capabilities. In this chapter, we generate another variation of the DVRP with traffic factor an...

—Artificial cover objects (ACOs) can be useful for surveying an area for snake abundance. However, very little is known about the correlation between environmental conditions, time of day, and ACO capture success rates. We studied the effects of time of day, temperature, humidity, wind speed, and sky cover variables in relation to ACO sampling capture rates of two colubrid species, Thamnophis b...

We introduce two reactive frameworks for dynamically adapting some parameters of an Ant Colony Optimization (ACO) algorithm. Both reactive frameworks use ACO to adapt parameters: pheromone trails are associated with parameter values; these pheromone trails represent the learnt desirability of using parameter values and are used to dynamically set parameters in a probabilistic way. The two frame...

Swarm Intelligent (SI) algorithms draw their inspiration from the interaction of individuals of social organisms. One such algorithm, Ant Colony Optimization (ACO) [1], utilizes the foraging behavior of ants to solve combinatorial optimization problems. Although ACO performs well in a static environment, it has been pointed out that ACO does not perform as well as other heuristics in dynamic si...

PURPOSE Comparisons of the characteristics of chronic obstructive pulmonary disease (COPD) and asthma-COPD overlap syndrome (ACOS) have been the focus of several studies since the diseases were defined by the Global Initiative for Asthma and Global Initiative for Chronic Obstructive Lung Disease guidelines. However, no consensus is available yet. In this study, we aimed to compare the character...

Ant Colony Optimization (ACO) was originally developed as an algorithmic technique for tackling NP-hard combinatorial optimization problems. Most of the research on ACO has focused on algorithmic variants that obtain high-quality solutions when computation time allows the evaluation of a very large number of candidate solutions, often in the order of millions. However, in situations where the e...

Ant colony optimization (ACO) algorithm is a new heuristic algorithm which has been demonstrated a successful technology and applied to solving complex optimization problems. But the ACO exists the low solving precision and premature convergence problem, particle swarm optimization (PSO) algorithm is introduced to improve performance of the ACO algorithm. A novel hybrid optimization (HPSACO) al...

<span lang="EN-US">This paper focuses on the optimal sizing of a positive second-generation current conveyor (CCII+), employing hybrid algorithm named DE-ACO, which is derived from combination differential evolution (DE) and ant colony optimization (ACO) algorithms. The basic idea this hybridization to apply DE for ACO algorithm’s initialization stage. Benchmark test functions were used e...

Financial marketing is a method of risky investment, but in shorter duration, the individual can expect profit or loss, depending on current market rate for organization. Though it has higher risk getting profit, many individuals are ready to take chance benefit. In era, much research going provide better prediction rate. Most researchers prefer Ant Colony Optimization (ACO) as more trustworthy...