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1. Modified Flower Pollination Algorithm for Global Optimization.

Author

Abdel-Basset, Mohamed, Mohamed, Reda, Saber, Safaa, Askar, S. S., and Abouhawwash, Mohamed

Subjects
*GLOBAL optimization, *POLLINATION, *MATHEMATICAL optimization, *ALGORITHMS, *DIFFERENTIAL evolution, *METAHEURISTIC algorithms
Abstract

In this paper, a modified flower pollination algorithm (MFPA) is proposed to improve the performance of the classical algorithm and to tackle the nonlinear equation systems widely used in engineering and science fields. In addition, the differential evolution (DE) is integrated with MFPA to strengthen its exploration operator in a new variant called HFPA. Those two algorithms were assessed using 23 well-known mathematical unimodal and multimodal test functions and 27 well-known nonlinear equation systems, and the obtained outcomes were extensively compared with those of eight well-known metaheuristic algorithms under various statistical analyses and the convergence curve. The experimental findings show that both MFPA and HFPA are competitive together and, compared to the others, they could be superior and competitive for most test cases. [ABSTRACT FROM AUTHOR]

Published
2021
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2. A novel hybrid firefly algorithm for global optimization

Author

Li-Qiang Liu, Xin-She Yang, Yun-Tao Dai, and Li-Na Zhang

Subjects
Optimization, Convergent Evolution, 0209 industrial biotechnology, Mathematical optimization, Evolutionary Processes, Population Size, Computer science, Population-based incremental learning, Population, Binomials, Evolutionary algorithm, lcsh:Medicine, Parallel Evolution, 02 engineering and technology, Research and Analysis Methods, Polynomials, 020901 industrial engineering & automation, Population Metrics, Computational Techniques, 0202 electrical engineering, electronic engineering, information engineering, Firefly algorithm, lcsh:Science, education, Global optimization, Evolutionary Biology, education.field_of_study, Multidisciplinary, Population Biology, Applied Mathematics, Simulation and Modeling, Population size, lcsh:R, Biology and Life Sciences, Particle swarm optimization, Maxima and minima, Algebra, Metaheuristic algorithms, Differential evolution, Physical Sciences, Benchmark (computing), lcsh:Q, 020201 artificial intelligence & image processing, Evolutionary Algorithms, Evolutionary Computation, Mathematics, Algorithms, Research Article
Abstract

Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate.

Published
2016