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A Fuzzy MARCOS-Based Analysis of dragonfly algorithm Variants in Industrial Optimization Problems

INFORMATICA 2024年第1期35卷 155-178页

作者： Kalita, Kanak Ganesh, Narayanan Shankar, Rajendran Chakraborty, Shankar Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Mech Engn Avadi India Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India Vellore Inst Technol Sch Comp Sci & Engn Chennai India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram India Jadavpur Univ Dept Prod Engn Kolkata India

Metaheuristics are commonly employed as a means of solving many distinct kinds of optimization problems. Several natural-process-inspired metaheuristic optimizers have been introduced in the recent years. The convergence, computational burden and statistical relevance of metaheuristics should be studied and compared for their potential use in future algorithm design and implementation. In this paper, eight different variants of dragonfly algorithm, i.e. classical dragonfly algorithm (DA), hybrid memory-based dragonfly algorithm with differential evolution (DADE), quantum-behaved and Gaussian mutational dragonfly algorithm (QGDA), memory-based hybrid dragonfly algorithm (MHDA), chaotic dragonfly algorithm (CDA), biogeography-based Mexican hat wavelet dragonfly algorithm (BMDA), hybrid Nelder-Mead algorithm and dragonfly algorithm (INMDA), and hybridization of dragonfly algorithm and artificial bee colony (HDA) are applied to solve four industrial chemical process optimization problems. A fuzzy multi-criteria decision making tool in the form of fuzzy-measurement alternatives and ranking according to compromise solution (MARCOS) is adopted to ascertain the relative rankings of the DA variants with respect to computational time, Friedman's rank based on optimal solutions and convergence rate. Based on the comprehensive testing of the algorithms, it is revealed that DADE, QGDA and classical DA are the top three DA variants in solving the industrial chemical process optimization problems under consideration.

关键词： dragonfly algorithm Process optimization MCDM MARCOS

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Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection

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APPLIED INTELLIGENCE 2019年第1期49卷 188-205页

作者： Sayed, Gehad Ismail Tharwat, Alaa Hassanien, Aboul Ella Cairo Univ Fac Comp & Informat Giza Egypt Frankfurt Univ Appl Sci Fac Comp Sci & Engn Frankfurt Germany Suez Canal Univ Fac Engn Ismailia Egypt SRGE Cairo Egypt

Selecting the most discriminative features is a challenging problem in many applications. Bio-inspired optimization algorithms have been widely applied to solve many optimization problems including the feature selection problem. In this paper, the most discriminating features were selected by a new Chaotic dragonfly algorithm (CDA) where chaotic maps embedded with searching iterations of the dragonfly algorithm (DA). Ten chaotic maps were employed to adjust the main parameters of dragonflies' movements through the optimization process to accelerate the convergence rate and improve the efficiency of DA. The proposed algorithm is employed for selecting features from the dataset that were extracted from the Drug bank database, which contained 6712 drugs. In this paper, 553 drugs that were bio-transformed into liver are used. This data have four toxic effects, namely, irritant, mutagenic, reproductive, and tumorigenic effect, where each drug is represented by 31 chemical descriptors. The proposed model is mainly comprised of three phases;data pre-processing, features selection, and the classification phase. In the data pre-processing phase, Synthetic Minority Over-sampling Technique (SMOTE) was used to solve the problem of the imbalanced dataset. At the features selection phase, the most discriminating features were selected using CDA. Finally, the selected features from CDA were used to feed Support Vector Machine (SVM) classifier at the classification phase. Experimental results proved the capability of CDA to find the optimal feature subset, which maximizing the classification performance and minimizing the number of selected features compared with DA and the other meta-heuristic optimization algorithms. Moreover, the experiments showed that Gauss chaotic map was the appropriate map to significantly boost the performance of DA. Additionally, the high obtained value of accuracy (81.82-96.08%), recall (80.84-96.11%), precision (81.45-96.08%) and F-Score (81.14-96.1%) f

关键词： Toxic effects dragonfly algorithm Feature selection Optimization algorithm Chaos theory

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Optimal tuning of 3 degree-of-freedom proportional-integral-derivative controller for hybrid distributed power system using dragonfly algorithm

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COMPUTERS & ELECTRICAL ENGINEERING 2018年 72卷 137-153页

作者： Guha, Dipayan Roy, Provas Kumar Banerjee, Subrate Motilal Nehru Natl Inst Technol Dept Elect Engn Allahabad Uttar Pradesh India Kalyani Govt Engn Coll Dept Elect Engn Kalyani W Bengal India Natl Inst Technol Durgapur Dept Elect Engn Durgapur W Bengal India

The objective of this paper is to study the dynamic stability of a hybrid energy distributed power system (HEDPS) subject to load and wind power variations. A three degree-of-freedom (3-DOF) proportional-integral-derivative (PID) controller is designed and implemented in the HEDPS to stabilize frequency and power fluctuations after the perturbation. For enhancing system dynamics, the parameters of the 3-DOF PID controller are optimized by using dragonfly algorithm (DA). The results are compared with the results obtained by Zeigler-Nichols tuning and some other well-known meta-heuristic algorithms. The efficacy of proposed DA over different reported algorithms is established in terms of convergence rate, minimum fitness value and dynamic performance of the system. The robustness of the 3-DOF PID-controller is ascertained with time-varying step load perturbation, random wind power perturbation, and under system parameter variation. The robust performance of proposed DA has also been established by performing statistical analysis. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Hybrid energy distributed power system Load frequency control 3DOF-PID controller dragonfly algorithm Battery energy storage system Probability density function

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Memory based Hybrid dragonfly algorithm for numerical optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 83卷 63-78页

作者： Ranjini, Sree K. S. Murugan, S. Indira Gandhi Ctr Atom Res Homi Bhabha Natl Inst Kalpakkam Tamil Nadu India Indira Gandhi Ctr Atom Res Remote Handling Irradiat & Robot Div Kalpakkam Tamil Nadu India

dragonfly algorithm (DA) is a recently proposed optimization algorithm based on the static and dynamic swarming behaviour of dragonflies. Due to its simplicity and efficiency, DA has received interest of researchers from different fields. However, it lacks internal memory which may lead to its premature convergence to local optima. To overcome this drawback, we propose a novel Memory based Hybrid dragonfly algorithm (MHDA) for solving numerical optimization problems. The pbest and gbest concept of Particle Swarm optimization (PSO) is added to conventional DA to guide the search process for potential candidate solutions and PSO is then initialized with pbest of DA to further exploit the search space. The proposed method combines the exploration capability of DA and exploitation capability of PSO to achieve global optimal solutions. The efficiency of the MHDA is validated by testing on basic unconstrained benchmark functions and CEC 2014 test functions. A comparative performance analysis between MHDA and other powerful optimization algorithms have been carried out and significance of the results is proved by statistical methods. The results show that MHDA gives better performance than conventional DA and PSO. Moreover, it gives competitive results in terms of convergence, accuracy and search-ability when compared with the state-of-the-art algorithms. The efficacy of MHDA in solving real world problems is also explained with three engineering design problems. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： dragonfly algorithm Particle Swarm Optimization Hybridization Benchmark functions Engineering problems Friedman's test

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Wind driven dragonfly algorithm for global optimization

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第6期33卷 e6054-e6054页

作者： Zhong, Lianlian Zhou, Yongquan Luo, Qifang Zhong, Keyu Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi High Sch Key Lab Complex Syst & Computat Nanning Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning Peoples R China

dragonfly algorithm (DA) is a new swarm intelligence optimization algorithm based on the static and dynamic swarm behavior of dragonflies. The algorithm has the characteristics of simple structure, strong search ability, easy implementation, and strong robustness. However, the DA algorithm itself also has insufficient solution accuracy and slow convergence speed. The Wind Driven Optimization algorithm (WDO) has the characteristics of fast convergence speed and strong global search capability. So as to improve the optimization performance of the DA algorithm and avoid premature convergence, the speed of the WDO is introduced into the later calculation of the algorithm iteration, which speeds up the convergence speed of the global optimal solution. This paper proposes a dragonfly algorithm based on wind driven (WDDA), that is to reduce the blindness of the dragonfly algorithm search, improve the solution accuracy and convergence speed, to improve the overall optimization performance of the algorithm. The 23 benchmark test functions and one engineering example for optimization and comparison experiments. The experimental results show that WDDA algorithm has better performance in function optimization.

关键词： dragonfly algorithm engineering optimization function optimization metaheuristic algorithm wind driven dragonfly algorithm

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Optimizing tribological performance in elevated temperature of TiC reinforced A356 matrix composites designed as automotive friction materials using dragonfly algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2023年第4期237卷 973-984页

作者： Simsek, Dogan Ozyurek, Dursun Ileri, Erol Karaoglan, Aslan Deniz Natl Def Univ Army NCO Vocat HE Sch Dept Automot Sci TR-10110 Balikesir Turkey Karabuk Univ Dept Mfg Engn Karabuk Turkey Balikesir Univ Dept Ind Engn Balikesir Turkey

The objective of this study designed in three steps is to evaluate the optimization of tribological performance in the elevated temperature of TiC reinforced A356 matrix composites, produced by mechanical alloying (MA), using the dragonfly algorithm (DA). In the first step, an experimental design is created to formulate the problem as an optimization problem with the help of regression modeling, and wear tests by adding a temperature module according to ASTM G99-05 standards are performed at various reinforced rates, operating temperatures, and loads. In the second step, regression models are fitted to the experimental results. In the third step, the dragonfly algorithm (DA) - one of the recent nature inspired metaheuristic optimization algorithms - has been adapted to optimize the formulated problem. The results of DA indicate that the optimum factor levels for the reinforced rate, operating temperature, and load are determined as 7%, 10 degrees C, and 20 N respectively to optimize the responses, namely weight loss, wear rate, and friction coefficient.

关键词： Composites elevated temperature TiC optimization dragonfly algorithm

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An optimized Continuous dragonfly algorithm Using Hill Climbing Local Search to Tackle the Low Exploitation Problem

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IEEE ACCESS 2022年 10卷 95030-95045页

作者： Emambocus, Bibi Aamirah Shafaa Jasser, Muhammed Basheer Sunway Univ Sch Engn & Technol Dept Comp & Informat Syst Petaling Jaya 47500 Malaysia

Optimization problems are usually solved using heuristic algorithms such as swarm intelligence algorithms owing to their ability of providing near-optimal solutions in a feasible amount of time. An example of an optimization problem is the training of artificial neural networks to obtain the most optimal connection weights. Artificial Neural Network (ANN), being the most prominent machine learning algorithm, has a multitude of applications in a myriad of areas. Recently, the use of ANNs has risen exponentially owing to its effective ability of making conclusions based on certain inputs. This ability is primarily achieved during the training phase of the ANN, which is a vital process prior to being able to use the ANN. Gradient descent-based algorithms, which are usually used for the training process, often encounter the problem of local optima, thus being unable to obtain the optimal connection weights of the ANN. Metaheuristic algorithms, including swarm intelligence algorithms, have been found to be a better alternative to train ANNs. The dragonfly algorithm (DA) is a swarm intelligence algorithm that has been found to be more effective than multiple swarm intelligence algorithms. However, despite having a good performance, it still suffers from low exploitation. In this paper, we propose to further improve the performance of DA by using hill climbing as a local search technique so as to enhance its low exploitation. The optimized DA algorithm is then used for training artificial neural networks which are employed for classification problems. Based on the experimental results, the optimized DA algorithm has higher effectiveness than the original DA and some other swarm intelligence algorithms as the ANNs trained by the optimized DA have a lower root mean squared error and a higher classification accuracy.

关键词： Artificial neural networks swarm intelligence dragonfly algorithm optimization

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Optimization of production schedules of multi-plants for dispatching ready-mix concrete trucks by integrating grey wolf optimizer and dragonfly algorithm

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ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT 2024年第11期31卷 4602-4624页

作者： Pham, Vu Hong Son Trang, Nguyen Thi Nha Dat, Chau Quang Ho Chi Minh City Univ Technol HCMUT Fac Civil Engn Ho Chi Minh City Vietnam Hanoi Univ Civil Engn HUCE Fac Construct Management & Econom Hanoi Vietnam

PurposeThe paper aims to provide an efficient dispatching schedule for ready-mix concrete (RMC) trucks and create a balance between batch plants and construction ***/methodology/approachThe paper focused on developing a new metaheuristic swarm intelligence algorithm using Java code. The paper used statistical criterion: mean, standard deviation, running time to verify the effectiveness of the proposed optimization method and compared its derivatives with other algorithms, such as genetic algorithm (GA), Tabu search (TS), bee colony optimization (BCO), ant lion optimizer (ALO), grey wolf optimizer (GWO), dragonfly algorithm (DA) and particle swarm optimization (PSO).FindingsThe paper proved that integrating GWO and DA yields better results than independent algorithms and some selected algorithms in the literature. It also suggests that multi-independent batch plants could effectively cooperate in a system to deliver RMC to various construction ***/valueThe paper provides a compelling new hybrid swarm intelligence algorithm and a model allowing multi-independent batch plants to work in a system to deliver RMC. It fulfills an identified need to study how batch plant managers can expand their dispatching network, increase their competitiveness and improve their supply chain operations.

关键词： Optimization Ready-mix concrete dispatching Swarm intelligence Hybrid algorithm dragonfly algorithm Grey wolf optimizer

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BMDA: applying biogeography-based optimization algorithm and Mexican hat wavelet to improve dragonfly algorithm

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SOFT COMPUTING 2020年第21期24卷 15979-16004页

作者： Shirani, Mohammad Reza Safi-Esfahani, Faramarz Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran

One of the methods for solving optimization problems is applying metaheuristic algorithms that find near to optimal solutions. dragonfly algorithm is one of the metaheuristic algorithms which search problem space by the inspiration of hunting and emigration behavior of dragonflies in nature. However, it suffers from the premature convergence of the population to an undesirable point in the detection ability (global search). In this research, an improved dragonfly algorithm called BMDA (applying Biogeography-based algorithm, Mexican hat wavelet, and dragonfly algorithm) is presented to resolve the premature convergence in high workloads by creating a mutation phase based on the combination of the biogeography-based optimization (BBO) migration process and the Mexican hat wavelet transform in dragonfly algorithm (DA). The algorithm was evaluated for the mean error in comparison with standard dragonfly algorithm (DA), Memory-based Hybrid dragonfly algorithm (MHDA), chaotic dragonfly algorithm version 9 (CDA9), Adaptive_DA algorithm, bat algorithm (BAT), particle swarm optimization algorithm (PSO), raven roosting optimization (RRO) and whale optimization algorithm (WOA) using the CEC2017 benchmark functions. The implementation results of the proposed BMDA algorithm applying different benchmark functions outweighed the DA-based algorithm, MHDA algorithm, CDA9 algorithm, Adaptive_DA algorithm, BAT algorithm, PSO algorithm, RRO, and WOA algorithms in terms of mean error.

关键词： dragonfly algorithm Biogeography-based optimization algorithm Mexican hat wavelet

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Construction of Prioritized T-Way Test Suite Using Bi-Objective dragonfly algorithm

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IEEE ACCESS 2022年 10卷 71683-71698页

作者： Ahmed, Mashuk Nasser, Abdullah B. Zamli, Kamal Z. Univ Malaysia Pahang Fac Comp Pekan 26600 Pahang Malaysia Univ Vaasa Sch Technol & Innovat Vaasa 65200 Finland

Software testing is important for ensuring the reliability of software systems. In software testing, effective test case generation is essential as an alternative to exhaustive testing. For improving the software testing technology, the t-way testing technique combined with metaheuristic algorithm has been great to analyze a large number of combinations for getting optimal solutions. However, most of the existing t-way strategies consider test case weights while generating test suites. Priority of test cases hasn't been fully considered in previous works, but in practice, it's frequently necessary to distinguish between high-priority and low-priority test cases. Therefore, the significance of test case prioritization is quite high. For this reason, this paper has proposed a t-way strategy that implements an adaptive dragonfly algorithm (DA) to construct prioritized t-way test suites. Both test case weight and test case priority have equal significance during test suite generation in this strategy. We have designed and implemented a Bi-objective dragonfly algorithm (BDA) for prioritized t-way test suite generation, and the two objectives are test case weight and test case priority. The test results demonstrate that BDA performs competitively against existing t-way strategies in terms of test suite size, and in addition, BDA generates prioritized test suites.

关键词： Testing Metaheuristics Licenses Software systems Genetic algorithms Credit cards Computer bugs Bi-objective dragonfly algorithm multi-objective optimization prioritized test suite test case priority t-way testing

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