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Comparative Implementation of the Benchmark Dejong 5 Function using Flower Pollination Algorithm and the African Buffalo Optimization


Affiliations
1 Department of Mathematical Sciences, Anchor University Lagos, Ipaja, Lagos, Nigeria
2 IBM Centre of Excellence, Universiti Malaysia Pahang, Kuantan 26300, Malaysia
 

This communication presents experimental research findings on the application of the flower pollination algorithm (FPA) and the African buffalo optimization (ABO) to implement the complex and fairly popular benchmark Dejong 5 function. The study aims to unravel the untapped potential of FPA and the ABO in providing good solutions to optimization problems. In addition, it explores the Dejong 5 function with the hope of attracting the attention of the research community to evaluate the capacity of the two comparative algorithms as well as the Dejong 5 function. We conclude from this study that in implementing FPA and ABO for solving the benchmark Dejong 5 problem, a population of 10 search agents and using 1000 iterations can produce effective and efficient outcomes.

Keywords

Benchmark, Comparative Implementation, Iteration, Optimization Algorithms, Search Agents, Test Functions.
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  • Comparative Implementation of the Benchmark Dejong 5 Function using Flower Pollination Algorithm and the African Buffalo Optimization

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Authors

Julius Beneoluchi Odili
Department of Mathematical Sciences, Anchor University Lagos, Ipaja, Lagos, Nigeria
A. Noraziah
IBM Centre of Excellence, Universiti Malaysia Pahang, Kuantan 26300, Malaysia

Abstract


This communication presents experimental research findings on the application of the flower pollination algorithm (FPA) and the African buffalo optimization (ABO) to implement the complex and fairly popular benchmark Dejong 5 function. The study aims to unravel the untapped potential of FPA and the ABO in providing good solutions to optimization problems. In addition, it explores the Dejong 5 function with the hope of attracting the attention of the research community to evaluate the capacity of the two comparative algorithms as well as the Dejong 5 function. We conclude from this study that in implementing FPA and ABO for solving the benchmark Dejong 5 problem, a population of 10 search agents and using 1000 iterations can produce effective and efficient outcomes.

Keywords


Benchmark, Comparative Implementation, Iteration, Optimization Algorithms, Search Agents, Test Functions.

References





DOI: https://doi.org/10.18520/cs%2Fv117%2Fi5%2F871-877