In this paper we wish to investigate how optimization problems involving angles can best be handled when using Differential Evolution (DE) as the optimization technique. Specifically we state the hypothesis that angles should not be recombined naïvly. ...

A new evolutionary-based algorithm is proposed to solve the robot manipulator optimal path generation problem. The following scenario is considered: given a learnt joint path describing a robot manipulator simple task in the Cartesian space, an optimal ...

In this paper we develop and test a novel stochastic algorithm SNE (Shrinking Neighborhood Evolution) based on the issue of bound constrained optimization problem. Its heuristic strategy is simple and direct-related to the search region of the solving ...

Bare Bones Particle Swarm Optimization (BBPSO) is a powerful algorithm, which has shown potential to solving multimodal optimization problems. Unfortunately, BBPSO may also get stuck into local optima when optimizing functions with many local optima in ...

In this paper, we describes the simultaneous perturbation particle swarm optimization which is a combination of the particle swarm optimization and the simultaneous perturbation optimization method. The method has global search capability of the ...

With the development of Graphics Processing Units (GPU) and the Compute Unified Device Architecture (CUDA) platform, several areas of knowledge are being benefited with the reduction of the computing time. Our goal is to show how optimization algorithms ...

In this paper, a new mechanism for dynamically varying the population size is proposed based on a previously modified PSO algorithm (nPSO). This new algorithm is extended to the multi-objective optimisation case by applying the Random Weighted ...

An investigation on control mechanism using particle swarm optimization (PSO) to suppress the vibration of flexible plate has been carried out. Active vibration control (AVC) is implemented for the case of single-input single output (SISO), and the ...

The new hp-HGS multi-deme, genetic strategy (hp-adaptive Finite Element Method combined with Hierarchic Genetic Strategy) for economic solving parametric inverse problems is presented in this paper. Inverse problems under consideration are formulated as ...

A number of population based optimization algorithms have been proposed in recent years to solve unconstrained and constrained single and multi-objective optimization problems. Most of such algorithms inherently prefer a feasible solution over an ...

Global optimization problems with numerous local and global optima are difficult to solve, which can trap traditional genetic algorithms. To solve the problems, a hybrid self-adaptive genetic algorithm based on sexual reproduction and Baldwin effect is ...

This paper introduces a particle swarm optimization algorithm with adaptive velocity (VPSO), in which a moving maximum limited velocity is set in original particle swarm optimization (PSO) algorithm to improve the performance of the PSO. The test ...

Particle Swarm Optimization (PSO) is a stochastic optimization approach that originated from earlier attempts to simulate the behavior of birds and was successfully applied in many applications as an optimization tool. Estimation of distributions ...

Support Vector Machines (SVMs) are considered state-of-the-art learning machines techniques for classification problems. This paper studies the training of SVMs in the special case of problems in which the raw data to be used for training purposes is ...

This work evaluates three evolutionary algorithms in a constraint satisfaction problem. Specifically, the problem is the Eternity II, a edge-matching puzzle with 256 unique square tiles that have to be placed on a square board of 16 × 16 cells. The aim ...

This paper describes a new technique for training microwave neural network models. The proposed technique combines quasi-Newton algorithm with a global optimization algorithm called Particle Swarm Optimization (PSO). The quasi-Newton process for ...

Evolutionary multi-objective optimization algorithms are commonly used to obtain a set of nondominated solutions for over a decade. Recently, a lot of emphasis have been laid on hybridizing evolutionary algorithms with MCDM and mathematical programming ...

In this paper, we propose a new heuristics called "group extinction". The heuristics is inspired by the existence of the extinction in the nature that groups of individuals, which have been consuming a large amount of the ecological resources, are not ...

Migration policies in distributed evolutionary algorithms are bound to have, as much as any other evolutionary operator, an impact on the overall performance. However, they have not been an active area of research until recently, and this research has ...

Differential Evolution (DE) is a novel evolutionary approach capable of handling non-differentiable, non-linear and multi-modal objective functions. DE has been consistently ranked as one of the best search algorithm for solving global optimization ...