Hybridization of Evolutionary Algorithms (original) (raw)

On the Hybridization of Evolutionary Algorithms and Optimization Techniques

Evolutionary Algorithms are powerful optimization techniques which have been applied to many different problems, from complex mathematical functions to real-world applications. Evolutionary computation, offers practical advantages to the researcher facing difficult optimization problems. These advantages are multifold, including the simplicity of the approach, its robust response to changing circumstance and its flexibility. Large combinatorial optimization problems often cannot be solved exactly. Approximations are obtained using local search. The main drawback in this strategy is that it often gets stuck in local optima, where the objective function can only be improved by making changes beyond the neighbors of the current state. This clearly paves way to the need for hybridization of evolutionary algorithms with other optimization algorithms and local search algorithms. This paper identifies different ways to efficiently hybridize the evolutionary algorithms with other search and optimization techniques to provide optimized solutions to engineering problems especially, the Wireless Sensor Networks (WSN).

Comprehensive Survey of the Hybrid Evolutionary Algorithms

International Journal of Applied Evolutionary Computation, 2013

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) and an improved non-dominating sorting multiobjective genetic algorithm (NSGA-II) is two well known multiobjective evolutionary algorithms (MOEAs) in the field of evolutionary computation. This paper mainly reviews their hybrid versions and some other algorithms which are developed for solving multiobjective optimization problems (MOPs. The mathematical formulation of a MOP and some basic definitions for tackling MOPs, including Pareto optimality, Pareto optimal set (PS), Pareto front (PF) are provided in Section 1. Section 2 presents a brief introduction to hybrid MOEAs. The authors present literature review in subsections. Subsection 2.1 provides memetic multiobjective evolutionary algorithms. Subsection 2.2 presents the hybrid versions of well-known Pareto dominance based MOEAs. Subsection 2.4 summarizes some enhanced Versions of MOEA/D paradigm. Subsection 2.5 reviews some multimethod search approaches dealing...

Hybrid evolutionary algorithms: methodologies, architectures, and reviews

2007

Evolutionary computation has become an important problem solving methodology among many researchers. The population-based collective learning process, selfadaptation, and robustness are some of the key features of evolutionary algorithms when compared to other global optimization techniques.

An Improved Hybrid Genetic Algorithm with a New Local Search Procedure

Journal of Applied Mathematics, 2013

One important challenge of a hybrid genetic algorithm (HGA) (also called memetic algorithm) is the tradeoff between global and local searching (LS) as it is the case that the cost of an LS can be rather high. This paper proposes a novel, simplified, and efficient HGA with a new individual learning procedure that performs a LS only when the best offspring (solution) in the offspring population is also the best in the current parent population. Additionally, a new LS method is developed based on a three-directional search (TD), which is derivative-free and self-adaptive. The new HGA with two different LS methods (the TD and Neld-Mead simplex) is compared with a traditional HGA. Four benchmark functions are employed to illustrate the improvement of the proposed method with the new learning procedure. The results show that the new HGA greatly reduces the number of function evaluations and converges much faster to the global optimum than a traditional HGA. The TD local search method is a...

Comprehensive Survey of Hybrid Evolutionary Algorithms

International Journal of Applied Evolutionary Computation (IJAEC), 2013

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) and an improved non-dominating sorting multiobjective genetic algorithm (NSGA-II) are two well known multiobjective evolutionary algorithms (MOEAs) in the field of evolutionary computation. This paper mainly reviews their hybrid versions and some other state-of-the-art EAs that are very recently developed for solving different types of multiobjective search and optimization problems .