Hybrid genetic algorithm for engineering design problems

Yan, Xuesong; Liu, Hanmin; Zhu, Zhixin

doi:10.1007/s10586-016-0680-8

Cited by 27 publications

(5 citation statements)

References 24 publications

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“…Then, the calculated velocity is added to the previous position of the particle and particle is moved to new position as in (2). After that, both the particle's personal best and the global best positions are updated if there is an improvement in the objective value (see (3) and 4). Finally, when the maximum number of iterations is reached, the best solution found is returned by the algorithm.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…Finding the shortest route in which every city is visited at once [1], optimal placement of wireless sensors for area coverage maximization [2], calculating the optimum structure of pressure vessels or welded beams as engineering design problems [3] are just a few examples of important optimization problems that may be encountered in daily life.…”

Section: Introductionmentioning

confidence: 99%

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A high-level and adaptive metaheuristic selection algorithm for solving high dimensional bound-constrained continuous optimization problems

Gökalp¹,

Uğur²

2020

Turk J Elec Eng & Comp Sci

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Metaheuristic algorithms are used to find sufficiently good solutions for the optimization problems that are not solvable in a polynomial time. Although metaheuristics offer a general problem-solving framework and can be applied to various types of optimization problems, their performances depend heavily on the problem to be solved. Thus, hybrid metaheuristics are used to combine strong parts of different algorithms. In this study, a novel adaptive metaheuristic selection algorithm is proposed for solving bound-constrained continuous optimization problems. The developed method hybridizes artificial bee colony, differential evolution, and particle swarm optimization at a high level where each algorithm works independently from each other. As a main contribution to the literature, adaptive selection at metaheuristic level among these three algorithms is achieved by using a rank-based credit assignment and UCB1 multiarmed bandit selection. The effectiveness of the developed algorithm has been evaluated on CEC'17 standard benchmark functions. The obtained numerical results indicate that the proposed algorithm outperforms the individual metaheuristics on which it is built and is more effective especially in high dimensional problems. It is also shown that the proposed algorithm is highly comparable with the related algorithms in the literature. Lastly, a case study that achieves adaptive selection of two good-performing algorithms (namely, covariance matrix adaptation evolution strategy and JADE) for the benchmark used in this study supports the effectiveness of the proposed method.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A high-level and adaptive metaheuristic selection algorithm for solving high dimensional bound-constrained continuous optimization problems

Gökalp¹,

Uğur²

2020

Turk J Elec Eng & Comp Sci

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“…Yan et al [78] worked on engineering design problems using a Cultural Evolution Algorithm (CEA). The paper presents four constrained engineering design problems; first one is design of tension/compression spring, design of pressure vessel issue, third is design of welded beam and fourth is satellite costs.…”

Section: Cultural Evolution Algorithmmentioning

confidence: 99%

Applicability and Efficiency of Socio-Cultural Inspired Algorithms in Optimizing Mechanical Systems – A Critical Review

Patel¹,

Kakandikar²,

Kulkarni³

2020

RCES

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In the recent years' optimization algorithms are used in many real-life applications to get the best out of everything. There are many types of optimization algorithm such as Nature inspired algorithm, Bio-Inspired algorithm which also have their applications. This paper aims to presents different category of algorithms and their applications, which are inspired by the social behavior of humans in groups termed as family. These algorithms are widely appreciated by researchers from various domains for optimization. The performance of social algorithm (SA) to discover the global optimum solution for the constrained, unconstrained and multi-objective optimization problems is sound. This paper extensively reviews and presents the applicability and also efficiency of the socio-inspired algorithm in solving real life problems in the field of mechanical engineering mechanical engineering problems.

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“…Given the undirected graph G, its clique C is a subgraph which consists of the GAs are heuristic and adaptive search methods which are based on "survival of fittest". Recently genetic algorithms are applied in discovering communities in complex social networks and solving constraint optimization problems based upon the principles of natural evolution [55][56]. The proper design of new genetic operators can reduce the exploration of problem search space and expected generations.…”

Section: Introductionmentioning

confidence: 99%

A New Multi-Objective Evolutionary Approach to Graph Coloring and Channel Allocation Problems

Balakrishnan¹,

Suresh²,

Marappan³

2021

JAMC

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Recent years, a large amount of graph coloring algorithms have been applied in different disciplines and engineering. This paper exhibits a new multi-objective evolutionary algorithm using the new evolutionary operators with multi-objectives in finding the solution to graph coloring and channel allocation. Clique, a maximal connected complete subgraph of a given graph is obtained in solving channel allocation problem. The outcomes of the devised evolutionary method are compared with other recent approaches. Multiple better gene sequences are selected and are applying the subsequent crossover and mutation operations with multiple objectives. The devised operators significantly minimize the problem search space and average generations compared to the standard genetic algorithm. The proposed operators achieve better solution compared to some of the existing well known methods. The expected performance measures are also increased while minimizing the expected generations. The proposed operators can also be applied in solving special engineering applications of graph coloring.

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Hybrid genetic algorithm for engineering design problems

Cited by 27 publications

References 24 publications

A high-level and adaptive metaheuristic selection algorithm for solving high dimensional bound-constrained continuous optimization problems

A high-level and adaptive metaheuristic selection algorithm for solving high dimensional bound-constrained continuous optimization problems

Applicability and Efficiency of Socio-Cultural Inspired Algorithms in Optimizing Mechanical Systems – A Critical Review

A New Multi-Objective Evolutionary Approach to Graph Coloring and Channel Allocation Problems

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