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A novel cuckoo search technique for solving discrete optimization problems

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Abstract

During the past decade, swarm intelligence (SI) techniques have received considerable recognition among researchers to solve continuous optimization problems. However, only few significant works have been reported in the literature to solve discrete optimization problems using SI techniques. Therefore, this paper proposes an improved SI technique, namely, discrete cuckoo search. As an application, the proposed technique is employed to solve a transposition cipher, and then the efficiency of the proposed technique is compared to the existing genetic algorithms. The obtained results indicate that the performance of the proposed technique is superior to genetic algorithms (as compared to genetic algorithm, cuckoo search is roughly 1.5 times faster and recovers 12% more number of key elements). Hence, the proposed technique can be utilized to solve various discrete optimization problems, e.g., for optimal placement of phaser measurement units in a power system, traveling salesman problem, graph coloring problem etc.

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Notes

  1. Candidate key means a potential key evolved by the search technique by searching the keyspace.

  2. For demonstration and comparison of various GA attacks on the transposition cipher, we calculate the energy of each individuals using weight tables of the respective GA attacks. For example, we use Table 2 in the case of the demonstration of the Clark GA attack, while in the case of the demonstration of Song et al GA attack, we use Table 3.

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Authors and Affiliations

  1. Discipline of Computer Science and Engineering, Indian Institute of Technology Indore, Indore, India

    Ashish Jain & Narendra S. Chaudhari

  2. Discipline of Computer Science and Engineering, Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Narendra S. Chaudhari

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  1. Ashish Jain
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  2. Narendra S. Chaudhari
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Correspondence to Ashish Jain.

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Jain, A., Chaudhari, N.S. A novel cuckoo search technique for solving discrete optimization problems. Int J Syst Assur Eng Manag 9, 972–986 (2018). https://doi.org/10.1007/s13198-018-0696-y

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