Memetic Differential Evolution with Baldwin Effect and Opposition-Based Learning

Opposition-Based Learning in Compact Differential Evolution

This paper proposes the integration of the generalized opposition based learning into compact Differential Evolution frameworks and tests its impact on the algorithmic performance. Opposition-based learning is a technique which has been applied, in several circumstances, to enhance the performance of Differential Evolution. It consists of the generation of additional points by means of a hyper-...

Service Composition Optimization Using Differential Evolution and Opposition-based Learning

The numbers of web services are increasing rapidly over the last decades. One of the most interesting challenges in using web services is the usage of service composition that allows users to select and invoke composite services. In addition, the characteristic of each service is distinguished based on the quality of service (QoS). QoS is utilized in optimizing decisive factors such as cost or ...

Centroid Opposition-Based Differential Evolution

The capabilities of evolutionary algorithms (EAs) in solving nonlinear and non-convex optimization problems are significant. Differential evolution (DE) is an effective population-based EA, which has emerged as very competitive. Since its inception in 1995, multiple variants of DE have been proposed with higher performance. Among these DE variants, opposition-based differential evolution (ODE) ...

OGDE3: Opposition-Based Third Generalized Differential Evolution

Multi-Objective Optimization (MOO) metaheuristics are commonly used for solving complex MOO problems characterized by non-convexity, multimodality, mixed-types variables, non-linearity, and other complexities. However, often metaheuristics suffer from slow convergence. Opposition-Based Learning (OBL) has been successfully used in the past for acceleration of single-objective metaheuristics. The...

A Hybrid Group Search Optimizer with Opposition-Based Learning and Differential Evolution