Evaluation of Genetic Algorithm’s Selection Methods

Pandey, Hari Mohan; Shukla, Anupriya; Chaudhary, Ankit; Mehrotra, Deepti

doi:10.1007/978-81-322-2752-6_72

Cited by 15 publications

(2 citation statements)

References 3 publications

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“…(1) Natural evolution. The GA is the representative of this kind of algorithm [7,8,9,10,11,12] In addition, the mutation based evolutionary strategy and evolutionary planning are not suitable for solving problems with abstract constraints and goals. Genetic planning is a variation of GA, the difference is that the individuals of this algorithm is a function.…”

Section: Meta Heuristic Algorithmsmentioning

confidence: 99%

VNE Strategy based on Chaotic Hybrid Flower Pollination Algorithm Considering Multi-criteria Decision Making

Jiang

Zhang

2022

Preprint

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With the development of science and technology and the need for Multi-Criteria Decision-Making (MCDM), the optimization problem to be solved becomes extremely complex. The theoretically accurate and optimal solutions are often difficult to obtain. Therefore, meta-heuristic algorithms based on multi-point search have received extensive attention. The flower pollination algorithm (FPA) is a new swarm intelligence meta-heuristic algorithm, which can effectively control the balance between global search and local search through a handover probability, and gradually attracts the attention of researchers. However, the algorithm still has problems that are common to optimization algorithms. For example, the global search operation guided by the optimal solution is easy to lead the algorithm into local optimum, and the vector-guided search process is not suitable for solving some problems in discrete space. Moreover, the algorithm does not consider dynamic multi-criteria decision problems well. Aiming at these problems, the design strategy of hybrid flower pollination algorithm for Virtual Network Embedding (VNE) problem is discussed. Combining the advantages of the Genetic Algorithm (GA) and FPA, the algorithm is optimized for the characteristics of discrete optimization problems. The cross operation is used to replace the cross-pollination operation to complete the global search and replace the mutation operation with self-pollination operation to enhance the ability of local search. Moreover, a

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Section: Meta Heuristic Algorithmsmentioning

confidence: 99%

VNE Strategy based on Chaotic Hybrid Flower Pollination Algorithm Considering Multi-criteria Decision Making

Jiang

Zhang

2022

Preprint

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“…The success of these algorithms are mainly depends on the parameters they used. For example, the GA's success depends on the crossover and mutation rate, selection method used [4][5][6]. Similarly, cooling rate is the key for the success of the Simulated Annealing (SA) [7].…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Pattern Search, Simulated Annealing, Genetic Algorithm and Jaya Algorithm

Pandey¹,

Rajput²,

Mishra³

2017

Advances in Intelligent Systems and Computing

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In this paper, we have shown the performance comparison of four powerful global optimization algorithms, namely Pattern Search, Simulated Annealing, Genetic Algorithm and Jaya Algorithm. All of these algorithms are used to find an optimum solution. The standard benchmark functions are utilized for the implementation. The results are collected and analyzed that helps to classify the algorithms according to their computational capability to solve the optimization problems.

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Genetic Algorithm and Particle Swarm Optimization: Analysis and Remedial Suggestions

Pandey

2017

Lecture Notes in Networks and Systems

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Evaluation of Genetic Algorithm’s Selection Methods

Cited by 15 publications

References 3 publications

VNE Strategy based on Chaotic Hybrid Flower Pollination Algorithm Considering Multi-criteria Decision Making

VNE Strategy based on Chaotic Hybrid Flower Pollination Algorithm Considering Multi-criteria Decision Making

Performance Comparison of Pattern Search, Simulated Annealing, Genetic Algorithm and Jaya Algorithm

Genetic Algorithm and Particle Swarm Optimization: Analysis and Remedial Suggestions

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