Comprehensive Survey of the Hybrid Evolutionary Algorithms

Khan, Wali Ullah

doi:10.4018/jaec.2013040101

Cited by 27 publications

(12 citation statements)

References 119 publications

(135 reference statements)

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“…Although the results are encouraging, better results with other methods such as the MOEA/D or a hybrid evolutionary algorithm [36][37][38][39] as substitutes for NSGA2 and SPEA2 is expected. CEDA Algorithms may also be used for solving MaOPs (Many-Objective Problems) where the there are more than four objectives to optimize.…”

Section: Discussionmentioning

confidence: 99%

A Posteriori Pareto Front Diversification Using a Copula-Based Estimation of Distribution Algorithm

Cheriet¹,

Cherif²

2015

ijacsa

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Abstract-We propose CEDA, a Copula-based Estimation of Distribution Algorithm, to increase the size, achieve high diversity and convergence of optimal solutions for a multiobjective optimization problem. The algorithm exploits the statistical properties of Copulas to produce new solutions from the existing ones through the estimation of their distribution. CEDA starts by taking initial solutions provided by any MOEA (Multi Objective Evolutionary Algorithm), construct Copulas to estimate their distribution, and uses the constructed Copulas to generate new solutions. This design saves CEDA the need of running an MOEA every time alternative solutions are requested by a Decision Maker when the found solutions are not satisfactory. CEDA was tested on a set of benchmark problems traditionally used by the community, namely UF1, UF2, ..., UF10 and CF1, CF2, ..., CF10. CEDA used along with SPEA2 and NSGA2 as two examples of MOEA thus resulting in two variants CEDA-SPEA2 and CEDA-NSGA2 and compare them with SPEA2 and NSGA2. The results of The experiments show that, with both variants of CEDA, new solutions can be generated in a significantly smaller without compromising quality compared to those found SPEA2 and NSGA2.

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Section: Discussionmentioning

confidence: 99%

A Posteriori Pareto Front Diversification Using a Copula-Based Estimation of Distribution Algorithm

Cheriet¹,

Cherif²

2015

ijacsa

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“…Bio-inspired methods are famous due to their unique way of searching, discovering and utilization which go beyond the cooperation of individual and society nature agents as well. A stable and sufficient amount of exploration and exploitation mean these methods are robust for finding a solution to a given problem [23,[37][38][39][40][41][42][43][44][45][46]. Exploration includes things captured by terms such as search, variation, risk taking, experimentation, play, flexibility, discovery, innovation, while exploitation includes such things as refinement, choice, production, efficiency, selection, implementation, execution.…”

Section: Gbest Guided Artificial Bee Colony Algorithmmentioning

confidence: 99%

Global Gbest Guided-Artificial Bee Colony Algorithm for Numerical Function Optimization

et al. 2018

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Numerous computational algorithms are used to obtain a high performance in solving mathematics, engineering and statistical complexities. Recently, an attractive bio-inspired method—namely the Artificial Bee Colony (ABC)—has shown outstanding performance with some typical computational algorithms in different complex problems. The modification, hybridization and improvement strategies made ABC more attractive to science and engineering researchers. The two well-known honeybees-based upgraded algorithms, Gbest Guided Artificial Bee Colony (GGABC) and Global Artificial Bee Colony Search (GABCS), use the foraging behavior of the global best and guided best honeybees for solving complex optimization tasks. Here, the hybrid of the above GGABC and GABC methods is called the 3G-ABC algorithm for strong discovery and exploitation processes. The proposed and typical methods were implemented on the basis of maximum fitness values instead of maximum cycle numbers, which has provided an extra strength to the proposed and existing methods. The experimental results were tested with sets of fifteen numerical benchmark functions. The obtained results from the proposed approach are compared with the several existing approaches such as ABC, GABC and GGABC, result and found to be very profitable. Finally, obtained results are verified with some statistical testing.

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“…EAs are population-based techniques and provide good approximated solutions in single simulation unlike the traditional mathematical programming techniques. EAs are population-based metaheuristics [7][8][9] and are generally divided into nine different groups: the biology-based [10,11], physics-based [12,13], social-based [14][15][16][17], music-based [18], chemical-based [19], sport-based [20], mathematics-based [21], swarm-based [22][23][24][25][26][27][28], and hybrid methods [29][30][31][32][33][34][35][36]. Genetic algorithm (GA) [37], evolutionary strategy (ES) [38], evolutionary programming (EP) [39,40], and genetic programming (GP) [41] are the classical paradigms of evolutionary computing [8].…”

Section: Introductionmentioning

confidence: 99%

A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems

2021

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Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms (EAs) were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are mainly categorized into nature-inspired and swarm-intelligence- (SI-) based paradigms. All these developed algorithms have some merits and also demerits. Particle swarm optimization (PSO), firefly algorithm, ant colony optimization (ACO), and bat algorithm (BA) have gained much popularity and they have successfully tackled various test suites of benchmark functions and real-world problems. These SI-based algorithms follow the social and interactive principles to perform their search process while approximating solution for the given problems. In this paper, a multiswarm-intelligence-based algorithm (MSIA) is developed to cope with bound constrained functions. The suggested algorithm integrates the SI-based algorithms to evolve population and handle exploration versus exploitation issues. Thirty bound constrained benchmark functions are used to evaluate the performance of the proposed algorithm. The test suite of benchmark function is recently designed for the special session of EAs competition in IEEE Congress on Evolutionary Computation (IEEE-CEC′13). The suggested algorithm has approximated promising solutions with good convergence and diversity maintenance for most of the used bound constrained single optimization problems.

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Comprehensive Survey of the Hybrid Evolutionary Algorithms

Cited by 27 publications

References 119 publications

A Posteriori Pareto Front Diversification Using a Copula-Based Estimation of Distribution Algorithm

A Posteriori Pareto Front Diversification Using a Copula-Based Estimation of Distribution Algorithm

Global Gbest Guided-Artificial Bee Colony Algorithm for Numerical Function Optimization

A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems

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