Recently, there has been significant research interest in homogeneous modular robots that can transform (i.e. reconfigure their overall shape). However; many of the proposed transformation mechanisms are too expensive and complex to be practical. The transformation process is also normally slow, and therefore the mechanisms are not suitable for sifuations where frequent, quick reconfiguration i...

This paper examines the problem of giving very simple swarm robots the capability of recognizing and locating other swarm members. The ability to recognize conspecific robots will allow swarms of robots to adjust their mutual separation as required by any specific task. For instance, robots engaging in flocking behavior will need to maintain a fixed mutual separation. One of the perceived benef...

in this paper, a new enhanced version of the particle swarm optimization (pso) is presented. an important modification is made by adding probabilistic functions into pso, and it is named probabilistic particle swarm optimization (ppso). since the variation of the velocity of particles in pso constitutes its search engine, it should provide two phases of optimization process which are: explorati...

Hard optimization problems that cannot be solved within reasonable time by standard, mathematical, deterministic methods are of great practical interest. Metaheuristics inspired by nature were recently successfully used for such problems. These metaheuristics are based on random Monte-Carlo search guided by simulation of some nature intelligence, especially evolution and swarm intelligence. One...

A novel model including social, environmental and economic benefits is proposed in hybrid thermal/wind power system and studied by Karush-Kuhn-Tucker and hybrid particle swarm optimization techniques. Our work is the first to develop social dispatch model by calculating risk caused by wind power. Then the novel multi-objective optimization model of social-environment-economic dispatch is establ...

Aimed at coping with the complexity of construction engineering cost evaluation, the advantages of rough set theory, particle swarm algorithm and BP neural network are integrated to put forward a new model of construction engineering cost evaluation, namely, the model of construction engineering cost evaluation of optimized particle swarm and BP neural network on the basis of rough set theory. ...

This paper proposes an algorithm for Public Key Cryptography (PKC) using the hybrid concept of two evolutionary algorithms, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) respectively. PSO alone are fast and easy to implement, they follow the procedures of common evolutionary algorithm and posses memory feature which is absent in GA making it more valuable. In GA whole population ...

In this work, we take the advantages of the particle swarm optimization method which belongs to the family of swarm intelligence algorithms to find improved solutions for delivering digital video content with enhanced quality of experience to the end users over error-prone multi-hop wireless networks. In video transmission over such wireless networks, many network-based (packet loss, delay, etc...

Fuzzy rule-based systems are expert systems whose performance is strongly related to the quality of their knowledge and the associated knowledge acquisition processes and thus, the design of effective learning techniques is considered a critical and major problem of these systems. Knowledge acquisition with a swarm intelligence approach is a recent learning strategy for the evolution of fuzzy r...