Recently, more and more researches have been conducted on the multi-robot system by applying bioinspired algorithms. Particle Swarm Optimization (PSO) is one of the optimization algorithms that model a set of solutions as a swarm of particles that spread in the search space. This algorithm has solved many optimization problems, but has a defect when it is applied on search tasking. As the time ...

Evolutionary algorithms are a frequently used technique for designing morphology and controller of a robot. However, a significant challenge for evolutionary algorithms is premature convergence to local optima. Recently proposed Novelty Search algorithm introduces a radical idea that premature convergence can be avoided by ignoring the original objective and searching for any novel behaviors in...

Several multi-parent crossover operators had been proposed to increase performance of genetic algorithms. The operators allow several parents to simultaneously take part in creating offspring. The operators need to balance between the two conflicting goals, exploitation and exploration. Strong exploitation allows fast convergence to succeed but can lead to premature convergence while strong exp...

Particle swarm optimization algorithm is easy to reach premature convergence in the solution process, and fall into the local optimal solution. Aiming at the problem, this paper proposes a particle swarm optimization algorithm with chaotic mapping (CM-PSO). The algorithms uses chaotic mapping function to optimize the initial state of population, improve the probability of obtain optimal solutio...

Integrated scheduling of handling/storage equipment in container terminals is an NP-hard problem which has been studied during past two decades consciously. Genetic algorithms (GAs) have been applied for this optimization problem in many researches. However, the GA is vulnerable to trap in a local optima (results in premature convergence). In this paper a fuzzy logic controller (FLC) is designe...

Particle swarm optimization (PSO) algorithm is a simple and powerful population based stochastic search algorithm for solving optimization problems in the continuous search domain. However, the general PSO is more likely to get stuck at a local optimum and thereby leading to premature convergence when solving practical problems. One solution to avoid premature convergence is adjusting the contr...

The artificial bee colony (ABC) algorithm has been a well-known swarm intelligence algorithm, which assimilates the cooperating behavior of bees when seeking for nectar sources. Aiming to improve the conventional ABC algorithm, we focus on the re-initialization phase. In this paper, an overall-degradation-oriented artificial bee colony (OD-ABC) algorithm is proposed, pursuing to fight against p...

Evolutionary Computation is an emergent field, which provides new heuristics to function optimization where traditional approaches make the problem computationally intractable. Exploration and exploitation of solution in the problem space are main issues affecting the performance of an evolutionary algorithm. Current enhancements attempt to balance exploitation and exploration to avoid prematur...

When a simple real-valued estimation of distribution algorithm (EDA) with Gaussian model and maximum likelihood estimation of parameters is used, it converges prematurely even on the slope of the fitness function. The simplest way of preventing premature convergence by multiplying the variance estimate by a constant factor k each generation is studied. Recent works have shown that when increasi...

This paper investigates dynamic and partially connected ring topologies for cellular Evolutionary Algorithms (cEA). We hypothesize that these structures maintain population diversity at a higher level and reduce the risk of premature convergence to local optima on deceptive, multimodal and NP-hard fitness landscapes. A general framework for modelling partially connected topologies is proposed a...