With the rapid development of swarm intelligence research field, a large number of algorithms in swarm intelligence are proposed one after another. The strong points and the drawbacks of a specific swarm intelligence algorithm becomes clear to be seen when the number of its application increases. To overcome the handicaps, some hybrid methods are invented. In this review, three hybrid swarm int...

numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of multi-objective optimization (moo) had arisen several years ago. due to the complexities in such type of problems powerful heuristic techniques were needed, which has been strongly satisfied by swarm intelligence (si) techniques. particle swarm optimization (pso) has ...

Assigning responsibilities to classes is among first and arguably most important steps when creating objectoriented software design. This step depends greatly on human judgment and experience. In this paper our objective is to automatize assigning responsibilities to classes using metaheuristic optimization algorithms. Four different algorithms (simple genetic algorithm, hill climbing, simulate...

This paper studies particle swarm optimization approach enriched by two versions of an extension aimed at gathering information during the optimization process. Application of these extensions, called memory mechanisms, increases computational cost, but it is spent to a benefit by incorporating the knowledge about the problem into the algorithm and this way improving its search abilities. The f...

Part of the knowledge and set of beliefs of a cognitive agent are its mental models of the world and of other agents. A mental model reflects the cultural context and past experiences of an adaptive agent. In interacting with other agents, we claim that a causal mental model, i.e. a cognitive map, which might be different for each agent, adapts and changes causing coalitions to emerge. Possible...

Categorizing the consecutive video frames into shots is the first step for content-based video retrieval. Recently, more and more research has made use of support vector machine to improve the performance of shot boundary detection. However, there has not been a uniform standard for selecting parameters of support vector machine kernel so that it relies on numerous experiences to try, which is ...

Nowadays, one of the prevalent diseases of 21 century is liver disorder annually killing so many people around the world. A range of therapies have been provided by researchers. Early diagnosis is of considerable significance in treating the disease. Diagnosis is of the physician's skills conducted based on their knowledge and experience yet an error might occur. Using various Artificial Intell...

Particle swarm optimization (PSO) is an optimization approach from the field of artificial intelligence. A population of so-called particles moves through the parameter space defined by the optimization problem, searching for good solutions. Inspired by natural swarms, the movements of the swarm members depend on own experiences and on the experiences of adjacent particles. PSO algorithms are m...

The concept of particle swarms originated from the simulation of the social behavior commonly observed in animal kingdom and evolved into a very simple but efficient technique for optimization in recent past. Since its advent in 1995, the Particle Swarm Optimization (PSO) algorithm has attracted the attention of a lot of researchers all over the world resulting into a huge number of variants of...

In this paper, an optimal stable algorithm is presented for members of a robots swarm moving toward a target. Equations of motion of the swarm are based on Lagrangian energy equations. Regarding of similar research On the design of swarm motion algorithm, an equation of motion considered constraints to guarantee no collision between the members and the members and obstacles along the motion pat...