The population diversity is an important issue in evolutionary optimization, playing the main role in exploring the search space. The aim of this paper is to analyze the influence of some diversity enhancing mechanisms on the ability of two algorithms related to evolutionary optimization, differential evolution and particle swarm optimization, to solve static and dynamic optimization problems.

Multi-objective evolutionary algorithms rely on the use of variation operators as their basic mechanism to carry out the evolutionary process. These operators are usually fixed and applied in the same way during algorithm execution, e.g., the mutation probability in genetic algorithms. This paper analyses whether a more dynamic approach combining different operators with variable application ra...

In recent years, renewable energy sources such as wind energy have been used as one of the most effective ways to reduce pollution emissions. In this paper, a summation based multi-objective differential evolution (SMODE) algorithm is used to optimize the economic emission dispatch problem with stochastic wind power. The Weibull probability distribution function is used to model the stochastic ...

While research on constrained optimization using evolutionary algorithms has been actively pursued, it has had to face the problem that the ability to solve multi-modal problems, which have many local solutions within a feasible region, is insufficient, that the ability to solve problems with equality constraints is inadequate, and that the stability and efficiency of searches is low. We propos...

Random speckle structured light can increase the texture information of object surface, so it is added in binocular stereo vision system to solve matching ambiguity problem caused by surface with repetitive pattern or no texture. To improve reconstruction quality, many current researches utilize multiple patterns for projection and use methods based on spatiotemporal correlation. This paper pre...

In literature, Clustered Shortest-Path Tree Problem (CluSPT) is an NP-hard problem. Previous studies focus on approximation algorithms which search for optimal solution in relatively large space. Thus, these consume a amount of computational resources while the quality obtained results lower than expected. order to enhance performance process, this paper proposes two different approaches are in...

This work details early research aimed at applying the powerful resource allocation mechanism deployed in Stochastic Diffusion Search (SDS) to the Differential Evolution (DE), effectively merging a nature inspired swarm intelligence algorithm with a biologically inspired evolutionary algorithm. The results reported herein suggest that the hybrid algorithm, exploiting information sharing between...

In the world of combinatorial optimization, a wide range of combinatorial problems exist for which the solution is permutative, such as knapsack problem, traveling salesman problem (TSP), vehicle routine problem (VRP), quadratic assignment problem (QAP), dynamic pickand-place (DPP) model of placement sequence and magazine assignment in robots and various types of scheduling problems. The object...

Differential Evolution (DE) is a random search algorithm used for function optimization basing on population evolution. It has been proved effective and efficient through years of studies. Among three evolutionary operations, the mutation scheme is believed to have high relation to convergence and diversity during the evolution process. This paper is going to introduce a modified differential e...

this paper presents a model for constrained multiobjective optimization of mixed-cropping planning. the decision challenges that are normally faced by farmers include what to plant, when to plant, where to plant and how much to plant in order to yield maximum output. consequently, the central objective of this work is to concurrently maximize net profit, maximize crop production and minimize pl...