Many real practical applications are often needed to find more than one optimum solution. Existing Evolutionary Algorithm (EAs) are originally designed to search the unique global value of the objective function. The present work proposed an improved niching based scheme named spatially neighbors best search technique combine with crowding-based differential evolution (SnbDE) for multimodal opt...

From within the variety of research that has been devoted to the adaptation of Differential Evolution to the solution of problems dealing with permutation variables, the Geometric Differential Evolution algorithm appears to be a very promising strategy. This approach is based on a geometric interpretation of the evolutionary operators and has been specifically proposed for combinatorial optimiz...

In recent years a new evolutionary algorithm for optimization in continuos spaces called Differential Evolution (DE) has developed. DE turns out to need only few evaluation steps to minimize a function. This makes it an interesting candidate for problem domains with high computational costs as for instance in the automatic generation of programs. In this paper a DE-based tree discovering algori...

The purpose of this research is to present a new mathematical modeling for a vehicle routing problem considering concurrently the criteria such as distance, weight, traffic considerations, time window limitation, and heterogeneous vehicles in the reverse logistics network for collection of expired products. In addition, we aim to present an efficient solution approach according to differential ...

Differential Evolution (DE) was very successful in solving the global continuous optimization problem. It mainly uses the distance and direction information from the current population to guide its further search. Estimation of Distribution Algorithm (EDA) samples new solutions from a probability model which characterizes the distribution of promising solutions. This paper proposes a combinatio...

Abstract   Many parameter-tuning algorithms have been proposed for training Fuzzy Wavelet Neural Networks (FWNNs). Absence of appropriate structure, convergence to local optima and low speed in learning algorithms are deficiencies of FWNNs in previous studies. In this paper, a Memetic Algorithm (MA) is introduced to train FWNN for addressing aforementioned learning lacks. Differential Evolution...

In this paper, we point out some shortcomings of Differential evolution algorithm (DE) with lower search efficiency and advance to the local optimal value easily. Combining with particle swarm algorithm (PSO)’s advantages of convergence rate, we put forward a new hybrid algorithm (DPM) to overcome these shortcomings. Instead of dividing all individuals into two equal size groups, in DPM algorit...

In this communication, we have made an attempt to design multiplier-less low-pass finite impulse response (FIR) filter with the aid of various mutation strategies of Differential Evolution (DE) algorithm. Impulse response coefficient of the designed FIR filter has been represented as sums or differences of powers of two. Performance of the proposed filter has been evaluated in terms of its freq...

The task of global optimization is to find a point where the objective function obtains its most extreme value. Differential evolution (DE) is a population-based heuristic approach that creates new candidate solutions by combining several points of the same population. The algorithm has three parameters: amplification factor of the differential variation, crossover control parameter and populat...