This paper focuses on three very similar evolutionary algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). While GA is more suitable for discrete optimization, PSO and DE are more natural for continuous optimization. The paper first gives a brief introduction to the three EA techniques to highlight the common computational procedures. The gener...

This paper presents a parameter-less optimization framework that uses the extended compact genetic algorithm (ECGA) and iterated local search (ILS), but is not restricted to these algorithms. The presented optimization algorithm (ILS+ECGA) comes as an extension of the parameter-less genetic algorithm (GA), where the parameters of a selecto-recombinative GA are eliminated. The approach that we p...

shaded pole induction motors, despite the simplicity and low cost, are difficult to analyze particularly in the equivalent circuit methods, due to the asymmetric structure, saturation, harmonics, inaccurate estimation of leakage inductances and skew. hence, 3d numerical methods could be very useful in the design and optimization stage. this paper presents the 3d transient finite element analysi...

this paper, presents a suitable control system to manage energy in distributed power generation system with a battery energy storage station and fuel cell. first, proper dynamic shape modeling is prepared. second, control system is proposed which is based on classic controller. this model is educated with genetic algorithm and particle swarm optimization. the proposed strategy is compared with ...

optimization of cutting conditions is a non-linear optimization with constraint and it is very important to the increase of productivity and the reduction of costs. in recent years, several evolutionary and meta-heuristic optimization algorithms were introduced. the cuckoo optimization algorithm (coa) is one of several recent and powerful meta-heuristics which is inspired by the cuckoos and the...

Interference threshold based on energy setting in cognitive radios is a non-convex optimization problem [1]. The convergence of optimization techniques like Genetic algorithm (GA) takes several iterations to fix this threshold. Here, an attempt made to use Differential evolution (DE) method for optimization after formulating the objective functions. The advantages with this method were three fo...

This paper proposes a multi-level optimization strategy for the Negative Selection Algorithm (NSA) detectors, based on both the Genetic Algorithms (GA) and clonal selection principle. The NSA is a natural immune response-inspired pattern discrimination method. In our hierarchical optimization scheme, the NSA detectors are first optimized by the GA to occupy the maximal coverage of the nonself s...

Genetic algorithms (GAs) have proved to be a versatile and eeective approach for solving combinatorial optimization problems. Nevertheless, there are many situations in which the simple GA does not perform particularly well, and various methods of hybridization have been proposed. These often involve incorporating other methods such as simulated annealing or local optimization as a post-process...

The first reported hybrid artificial neural network-genetic algorithm (ANN-GA) approach for the optimization of on-capillary dipeptide derivatization is presented. More specifically, genetic optimization proved valuable in the determination of effective network structure with three defined parameter inputs: (i) phthalic anhydride injection volume, (ii) time of injection, and (iii) voltage, for ...

-A concept of a bi-population scheme for real-coded GAs consisting of an explorer sub-GA and an exploiter sub-GA is proposed. The explorer sub-GA mainly does exploration so as to avoid being trapped in local optima by means of restart mechanism; and the exploiter sub-GA does exploitation by which search can be performed more precisely in the neighborhood of the best solution obtained so far. An...