Summary. 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. Even though evolutionary computation has been widely accepted for solving several important practical applications in engineering, business, commerce, etc., yet in practice sometimes they deliver only marginal performance. Inappropriate selection of various parameters, representation, etc. are frequently blamed. There is little reason to expect that one can find a uniformly best algorithm for solving all optimization problems. This is in accordance with the No Free Lunch theorem, which explains that for any algorithm, any elevated performance over one class of problems is exactly paid for in performance over another class. Evolutionary algorithm behavior is determined by the exploitation and exploration relationship kept throughout the run. All these clearly illustrates the need for hybrid evolutionary approaches where the main task is to optimize the performance of the direct evolutionary approach. Recently, hybridization of evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty, and vagueness. In this chapter, first we emphasize the need for hybrid evolutionary algorithms and then we illustrate the various possibilities for hybridization of an evolutionary algorithm and also present some of the generic hybrid evolutionary architectures that has evolved during the last couple of decades. We also provide a review of some of the interesting hybrid frameworks reported in the literature.

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Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

Grey wolf optimization applied to economic load dispatch problems

Optimal VAR control for improvements in voltage profiles and for real power loss minimization using Biogeography Based Optimization

A new hybrid algorithm for optimal power flow considering prohibited zones and valve point effect

This paper presents biogeography based optimization (BBO) technique for solving constrained optimal power flow problems in power systems, considering valve point nonlinearities of generators. In this paper, the proposed algorithm has been tested in 9-bus and IEEE 30-bus systems under various simulated conditions. A comparison of simulation results reveals optimization efficacy of the proposed scheme over evolutionary programming (EP), genetic algorithm (GA), particle swarm optimization (PSO), mixed-integer particle swarm optimization (MIPSO) and sequential quadratic programming (SQP) used in MATPOWER for the global optimization of multi-constraint OPF problems.

Biogeography based optimization for multi-constraint optimal power flow with emission and non-smooth cost function

The basic evolutionary programming (BEP) method utilizes individual parent fitness to generate offspring. This is objectionable in many optimization problems, where the fitness value grows rapidly with problem dimensions, and two optimization problems differ by simply a scale factor. This paper is concerned with the development of an evolutionary programming method, which is functionally, and structurally equivalent to BEP, but still can be used effectively to optimize functions having strong fitness dependency between parents and their offspring. In this paper, a fitness-blind mutation (FBM) algorithm has been proposed, and then this is used in conjunction with the BEP mutation operator. The FBM operation has been implemented by taking the standard deviation of the Gaussian variable to vary in proportion to the genotypic distance between the individual parent and the fittest individual, which is defined as a pseudo-global optimum individual in a population pool. Also, the directionality of the random variation has been exploited to improve the probability of getting better solutions. In addition to this, the importance of initial search width for generating the offspring has been established empirically. The effectiveness of the proposed algorithm has been verified on well-established test functions.

A.S. Morris

Papers

A novel hybrid evolutionary programming method for function optimization