Abstract
The dragonfly algorithm (DA) is one of the optimization techniques developed in recent years. The random flying behavior of dragonflies in nature is modeled in the DA using the Levy flight mechanism (LFM). However, LFM has disadvantages such as the overflowing of the search area and interruption of random flights due to its big searching steps. In this study, an algorithm, known as the Brownian motion, is used to improve the randomization stage of the DA. The modified DA was applied to 15 single-objective and 6 multiobjective problems and then compared with the original algorithm. The modified DA provided up to 90% improvement compared to the original algorithm's minimum point access. The modified algorithm was also applied to welded beam design, a well-known benchmark problem, and thus was able to calculate the optimum cost 20% lower.
Highlights
Swarm-inspired optimization algorithms have successfully solved a lot of real-world problems: In 2013, the artificial bee colony (ABC) algorithm was used for data collection in wireless sensor networks [1], and the ant colony optimization (ACO) algorithm was used for multicompartment vehicle routing problems [2]
In 2017, the multilevel image thresholding problem was solved with the elephant herding optimization (EHO) algorithm [7], and the ACO algorithm was used in estimating transportation energy demand in Turkey [8]. e dragonfly algorithm (DA) was used in the synthesis of concentric circular antenna arrays by Babayigit [9], and the EHO algorithm was used in support vector machine parameter tuning [10]
Hybrid optimization algorithms solved many problems in previous studies: In 2010, the ACO algorithm for solving a complex combinatorial optimization problem was modified by Yang and Zhuang [12], and the particle swarm optimization (PSO) for nonconvex economic dispatch problems was improved by Roh et al [13]
Summary
Swarm-inspired optimization algorithms have successfully solved a lot of real-world problems: In 2013, the artificial bee colony (ABC) algorithm was used for data collection in wireless sensor networks [1], and the ant colony optimization (ACO) algorithm was used for multicompartment vehicle routing problems [2]. Hybrid optimization algorithms solved many problems in previous studies: In 2010, the ACO algorithm for solving a complex combinatorial optimization problem was modified by Yang and Zhuang [12], and the particle swarm optimization (PSO) for nonconvex economic dispatch problems was improved by Roh et al [13]. Is recommendation often prevents the best solution from getting stuck in the local best of problems Another important benefit of random motion is the success of leaving no scanned space in the search space. One of the new solutions to hybridize optimization algorithms with a random flight method is the Levy flight mechanism (LFM). An improvement was achieved with LFM, and successful results were obtained due to the problem of early convergence of the agents during the optimization and localization of the agents. To the problem in PSO, they predicted that the lack of location of the wolves caused local minimization and solved this problem with the LFM
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