Hybrid Nelder–Mead Algorithm and Dragonfly Algorithm for Function Optimization and the Training of a Multilayer Perceptron

Jia, Xu; Fu, Yan

doi:10.1007/s13369-018-3536-0

Cited by 43 publications

(42 citation statements)

References 41 publications

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“…A huge number of social interactions in DA causes trapping into local optima, solving problems with less accuracy, and with an improper balance of exploitation and exploration. In reference [27], to overcome these deficiencies, DA combined by a better type of the Nelder-Mead algorithm or so-called INMDA for making the ability of local exploration more powerful, and avoid falling into local optima, the INMDA can be divided into two steps, the first step, DA utilized to explore the solution space. It provided the necessary variety to the artificial dragonflies to find the global optimum.…”

Section: Hybridized Versions Of Dragonfly Algorithmmentioning

confidence: 99%

A Survey on Dragonfly Algorithm and its Applications in Engineering

Rahman¹,

Rashid²

2020

Preprint

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Dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examine the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with some techniques, the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.

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Section: Hybridized Versions Of Dragonfly Algorithmmentioning

confidence: 99%

A Survey on Dragonfly Algorithm and its Applications in Engineering

Rahman¹,

Rashid²

2020

Preprint

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“…To further show the signi cant superiority of DDS-POBL, we compared it with eight state-of-the-art optimization algorithms, i.e., the particle swarm optimizer (PSO) [28], the piecewise opposition-based learning (POHS) [20], the completely derandomized self-adaptation evolution strategies (CMA-ES) [29], the composite differential evolution (CoDE) [30], the memory-based hybrid dragonfly algorithm (MHDA) [28], the exploration-enhanced grey wolf optimizer (EEGWO) [24], the hybrid method based on the DA and the improved NM simplex algorithm (INMDA) [13], and the firefly algorithm with neighborhood attraction (NaFA) [31]. In this experiment, the parameter settings of EEGWO, INMDA, and DDS-POBL are as follows: the population size is 30, the maximum number of iterations is 500, the number of independent experiments is 50, and the other parameters related to the algorithm are consistent with its original literature.…”

Section: Comparison With Other State-of-the Art Algorithmmentioning

confidence: 99%

“…Some of the famous single solutionbased heuristic global search algorithms are simulated annealing (SA) [2], threshold accepting method (TA) [3], microcanonical Annealing (MA) [4], tabu search (TS) [5], guided local search (GLS) [6], and dynamically dimensioned search (DDS) [7,8]. Population-based ones include evolutionary algorithms (EA) [9], genetic algorithms (GA) [10], particle swarm optimization (PSO) [11], dragonfly algorithm (DA) [12,13], and shuffled complex evolution (SCE) algorithms [14].…”

Section: Introductionmentioning

confidence: 99%

Dynamically Dimensioned Search Embedded with Piecewise Opposition-Based Learning for Global Optimization

Jia

Yun

et al. 2019

Scientific Programming

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Dynamically dimensioned search (DDS) is a well-known optimization algorithm in the field of single solution-based heuristic global search algorithms. Its successful application in the calibration of watershed environmental parameters has attracted researcher’s extensive attention. The dynamically dimensioned search algorithm is a kind of algorithm that converges to the global optimum under the best condition or the good local optimum in the worst case. In other words, the performance of DDS is easily affected by the optimization conditions. Therefore, this algorithm has also suffered from low robustness and limited scalability. In this work, an improved version of DDS called DDS-POBL is proposed. In the DDS-POBL, two effective methods are applied to improve the performance of the DDS algorithm. Piecewise opposition-based learning is introduced to guide DDS search in the right direction, and the golden section method is used to search for more promising areas. Numerical experiments are performed on a set of 23 classic test functions, and the results represent significant improvements in the optimization performance of DDS-POBL compared to DDS. Several experimental results using different parameter values demonstrate the high solution quality, strong robustness, and scalability of the proposed DDS-POBL algorithm. A comparative performance analysis between the DDS-POBL and other powerful algorithms has been carried out by statistical methods by using the significance of the results. The results show that DDS-POBL works better than PSO, CoDA, MHDA, NaFA, and CMA-ES and gives very competitive results when compared to INMDA and EEGWO. Moreover, the parameter calibration application of the Xinanjiang model shows the effectiveness of the DDS-POBL in the real optimization problem.

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“…In other words, one approach may show very promising results on a particular class of problems, but the same algorithm may show poor results on a different set of problems [24]. Therefore, more researchers improve the current approaches or propose new meta-heuristics for solving different complex problems every year, such as the dragonfly algorithm, is hybridized with the improved Nelder-Mead algorithm (INMDA) for function optimization and multilayer perceptron training [25], the dynamically dimensioned search is improved by embedding with piecewise opposition-based learning (DDS-POBL) for global optimization [26], and this also motivates our attempts in this paper to improve the GWO algorithm for solving complex ELD problems.…”

Section: Introductionmentioning

confidence: 99%

Noninferior Solution Grey Wolf Optimizer with an Independent Local Search Mechanism for Solving Economic Load Dispatch Problems

Jia

Yun

et al. 2019

Energies

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The economic load dispatch (ELD) problem is a complex optimization problem in power systems. The main task for this optimization problem is to minimize the total fuel cost of generators while also meeting the conditional constraints of valve-point loading effects, prohibited operating zones, and nonsmooth cost functions. In this paper, a novel grey wolf optimization (GWO), abbreviated as NGWO, is proposed to solve the ELD problem by introducing an independent local search strategy and a noninferior solution neighborhood independent local search technique to the original GWO algorithm to achieve the best problem solution. A local search strategy is added to the standard GWO algorithm in the NGWO, which is called GWOI, to search the local neighborhood of the global optimal point in depth and to guarantee a better candidate. In addition, a noninferior solution neighborhood independent local search method is introduced into the GWOI algorithm to find a better solution in the noninferior solution neighborhood and ensure the high probability of jumping out of the local optimum. The feasibility of the proposed NGWO method is verified on five different power systems, and it is compared with other selected methods in terms of the solution quality, convergence rate, and robustness. The compared experimental results indicate that the proposed NGWO method can efficiently solve ELD problems with higher-quality solutions.

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Hybrid Nelder–Mead Algorithm and Dragonfly Algorithm for Function Optimization and the Training of a Multilayer Perceptron

Cited by 43 publications

References 41 publications

A Survey on Dragonfly Algorithm and its Applications in Engineering

A Survey on Dragonfly Algorithm and its Applications in Engineering

Dynamically Dimensioned Search Embedded with Piecewise Opposition-Based Learning for Global Optimization

Noninferior Solution Grey Wolf Optimizer with an Independent Local Search Mechanism for Solving Economic Load Dispatch Problems

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