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COLMA: a chaos-based mayfly algorithm with opposition-based learning and Levy flight for numerical optimization and engineering design

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JOURNAL OF SUPERCOMPUTING 2023年第17期79卷 19699-19745页

作者： Zhao, Yanpu Huang, Changsheng Zhang, Mengjie Lv, Cheng China Univ Petr East China Sch Econ & Management Qingdao 266580 Shandong Peoples R China Dareway Software Co Ltd Jinan 250200 Shandong Peoples R China

The mayfly algorithm (MA) is a widely used metaheuristic algorithm characterized by a simple structure with simple parameters. However, MA may have problems such as poor global search ability and tend to fall into local optima. To overcome these limitations, this paper presents a chaos-based mayfly algorithm with opposition-based learning and Levy flight (COLMA) to boost the global search and local exploitation performance. In COLMA, we first introduced tent chaos to optimize the initialization process of the mayfly population, as random initialization processes may result in low diversity of the mayfly population. In addition, the gravity coefficient has been replaced with an adaptive gravity coefficient to balance the global search ability and local exploitation ability during the iterative process of the algorithm. In the process of updating the position of the male mayfly population, an opposition-based learning strategy based on an iterative chaotic map with infinite collapses is adopted to prevent the male mayfly population from falling into local optima. At the same time, in order to solve the problem of small search range of female mayfly population, Levy flight strategy was introduced to replace random walk strategy. Finally, an offspring optimization strategy was proposed to increase the probability of the offspring mayfly population approaching the optimal solution. To verify the effectiveness and superiority of COLMA and the adopted strategy, experiments were conducted on the classical benchmark functions, CEC 2017 benchmark suite and CEC 2020 real-world constraint optimization problems, and the results were statistically tested using the Wilcoxon signed rank test and Friedman test. The analysis results show that the proposed COLMA has statistical validity and reliability and has great advantages compared with MA, variant MA in terms of optimization accuracy, stability and convergence speed.

关键词： mayfly algorithm Metaheuristic algorithm Levy flight Opposition-based learning Numerical optimization Engineering design

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Automatic detection of breast cancer in ultrasound images using mayfly algorithm optimized handcrafted features

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JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022年第4期30卷 751-766页

作者： Vijayakumar, K. Rajinikanth, V Kirubakaran, M. K. St Josephs Inst Technol Dept Comp Sci & Engn Chennai 600119 Tamil Nadu India St Josephs Coll Engn Dept Elect & Instrumentat Engn Chennai Tamil Nadu India St Josephs Inst Technol Dept Informat Technol Chennai Tamil Nadu India

BACKGROUND: The incidence rates of breast cancer in women community is progressively raising and the premature diagnosis is necessary to detect and cure the disease. OBJECTIVE: To develop a novel automated disuse detection framework to examine the Breast-Ultrasound-Images (BUI). METHODS: This scheme includes the following stages;(i) Image acquisition and resizing, (ii) Gaussian filter-based preprocessing, (iii) Handcrafted features extraction, (iv) Optimal feature selection with mayfly algorithm (MA), (v) Binary classification and validation. The dataset includes BUI extracted from 133 normal, 445 benign and 210 malignant cases. Each BUI is resized to 256x256x1 pixels and the resized BUIs are used to develop and test the new scheme. Handcrafted feature-based cancer detection is employed and the parameters, such as Entropies, Local-Binary-Pattern (LBP) and Hu moments are considered. To avoid the over-fitting problem, a feature reduction procedure is also implemented with MA and the reduced feature sub-set is used to train and validate the classifiers developed in this research. RESULTS: The experiments were performed to classify BUIs between (i) normal and benign, (ii) normal and malignant, and (iii) benign and malignant cases. The results show that classification accuracy of > 94%, precision of > 92%, sensitivity of > 92% and specificity of > 90% are achieved applying the developed new schemes or framework. CONCLUSION: In this work, a machine-learning scheme is employed to detect/classify the disease using BUI and achieves promising results. In future, we will test the feasibility of implementing deep-learning method to this framework to further improve detection accuracy.

关键词： Breast cancer ultrasound image handcrafted features mayfly algorithm binary classification

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An improved hybrid mayfly algorithm for global optimization

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JOURNAL OF SUPERCOMPUTING 2023年第6期79卷 5878-5919页

作者： Yan, Zheping Yan, Jinyu Wu, Yifan Zhang, Chao Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China

As the complexity of optimization problems increases, metaheuristic algorithms play an important role in dealing with complex computational problems and try to find the best solution from all feasible solutions to the problem. The mayfly algorithm is a novel metaheuristic algorithm based on the social behavior of biological groups. The algorithm achieves global and local search by simulating the flight behavior and mating process of mayflies to obtain global optimal solution. However, the traditional mayfly algorithm has problems such as low convergence accuracy and poor stability and is prone to becoming trapped in local optimality. Aiming at the problem of the mayfly algorithm, an improved mayfly algorithm combined with the gray wolf optimization algorithm (MA-GWO) is proposed. In the mayfly algorithm, the Levy flight strategy and the hunting mechanism of the gray wolf optimization algorithm are introduced to achieve complementary advantages. To verify the superiority of the proposed algorithm, 19 classical benchmark functions, CEC-C06 2019 test functions and 5 engineering design problems are compared with various advanced metaheuristic algorithms. The experimental data show that the MA-GWO algorithm has significant enhancements over the traditional mayfly algorithm. In several test cases, especially for high-dimensional optimization problems, the MA-GWO algorithm is far superior to other metaheuristic algorithms, has better convergence and stability, and is an effective and feasible algorithm.

关键词： mayfly algorithm Levy flight strategy Grey wolf optimization Benchmark test functions Metaheuristic algorithms

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Optimization of hydraulic parameters for pipeline system of hydropower station with super long headrace tunnel based on mayfly algorithm considering operational scenarios

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ENERGY SCIENCE & ENGINEERING 2024年第6期12卷 2341-2364页

作者： Wang, Le Guo, Wencheng Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China

This paper studies the optimization of hydraulic parameters for pipeline system of hydropower station with super long headrace tunnel (HSSLHT) based on mayfly algorithm considering operational scenarios. Firstly, the state equation of HSSLHT under load disturbance is derived. The optimization design of hydraulic parameters for pipeline system based on mayfly algorithm is proposed. Then, the optimization of hydraulic parameters is conducted and analyzed. Finally, the effects of pipeline diameters and transfer coefficients of turbines on the optimization of hydraulic parameters for pipeline systems are revealed. The results show that the optimization of hydraulic parameters for pipeline system is a multiobjective problem, and the several objective functions exhibit significant conflicts. Compared to the firefly algorithm and genetic algorithm, the objective function under mayfly algorithm is improved by 14.7% and 5.1%, respectively. The mayfly algorithm can make the hydraulic parameters for the pipeline system reach the Pareto optimal solution under both load decrease condition and load increase condition. The diameter of penstock has an obvious influence on the robustness of dynamic performance of HSSLHT. When the diameter of penstock increases by 30%, the robustness of HSSLHT becomes worse and the robustness index deteriorates by 57%. The reason is that the flow inertia of penstock becomes smaller with the increase of diameter, and the flow inertia of penstock is favorable for resisting disturbance of HSSLHT. The coefficient of throttled orifice head loss and the lengths and head losses of headrace tunnel and penstock are the key hydraulic parameters for matching the operation of HSSLHT. This paper studies the optimization of hydraulic parameters for pipeline system of hydropower station with super long headrace tunnel (HSSLHT) based on mayfly algorithm considering operational scenarios. Firstly, the state equation of HSSLHT under load disturbance is derived. The op

关键词： hydraulic parameters hydropower station mayfly algorithm multiobjective optimization pipeline system

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A Novel Artificial Neural Network (ANN) Using The mayfly algorithm for Classification

A Novel Artificial Neural Network (ANN) Using The Mayfly Alg...

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IEEE International Conference on Digital Futures and Transformative Technologies (ICoDT2)

作者： Moosavi, Syed Kumayl Raza Zafar, Muhammad Hamza Akhter, Malik Naveed Hadi, Shahzaib Farooq Khan, Noman Mujeeb Sanfilippo, Filippo Natl Univ Sci & Technol Islamabad Pakistan Capital Univ Sci & Technol Islamabad Pakistan Univ Agder UiA Dept Engn Sci Grimstad Norway

ISBN: (纸本)9781665412858

Training of Artificial Neural Networks (ANNs) have been improved over the years using meta heuristic algorithms that introduce randomness into the training method but they might be prone to falling into a local minima in a high-dimensional space and have low convergence rate with the iterative process. To cater for the inefficiencies of training such an ANN, a novel neural network is presented in this paper using the bio-inspired algorithm of the movement and mating of the mayflies. The proposed mayfly algorithm is explored as a means to update weights and biases of the neural network. As compared to previous meta heuristic algorithms, the proposed approach finds the global minima cost at far less number of iterations and with higher accuracy. The network proposed, which is named mayfly algorithm based Neural Network (MFANN) consists of an input layer, a single hidden layer of 10 neurons and an output layer. Two University of California Irvine (UCI) database sample datasets have been used as benchmark for this study, namely the Banknote Authentication (BA) and the Cryotherapy, for which the training accuracy achieved is 99.2350% and 96.6102%, whereas the Testing accuracy is 99.1247% and 90% respectively. Comparative study with grey wolf optimization neural network (GWONN) and particle swarm optimization neural network (PSONN) reveal that the proposed MFANN achieves 1-2% better accuracy with training dataset and 2% better accuracy with testing dataset.

关键词： bio-inspired neural network intelligent control system mayfly algorithm training algorithms classification

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A Comprehensive Study on a Meta Heuristics Optimization algorithm- Hybridized mayfly algorithm 6

A Comprehensive Study on a Meta Heuristics Optimization Algo...

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6th International Conference on Trends in Electronics and Informatics (ICOEI)

作者： Chandra, B. Ravi Kumar, Krishan LPU Dept Elect & Commun Engg Phagwara Punjab India

ISBN: (纸本)9781665483285;9781665483292

Several optimization techniques have been proposed to solve the optimization problem. This study introduces a novel method known as the mayfly algorithm (MA), which is one of the swarm optimization algorithms. Every problem in the environment has an answer at its core. Based on this, researchers in the present day are preparing one another for the intelligence of nature. Given that the mayfly algorithm (MA) was recently introduced in the year 2020. The proposed algorithm is based on the process of mayflies mating and flying behaviour with the key benefits of both the evolutionary algorithm and swarm optimization. This mayfly algorithm is introduced as a result of the PSO's good hybridization and superior algorithm with the structure of genetic algorithm and firefly algorithm. According to several research studies, the mayfly algorithm outperforms other prominent Metaheuristic algorithms. The exceptional performance of mayfly algorithm is demonstrated by comparing the results in terms of the rate of convergence speed. This article will be a great resource for book reviewers to gain good understanding on the upcoming advanced mayfly algorithm in which a lot of debate has been done on both the sides by analyzing its implementations, applications, and recent advancements for enhancing the proposed mayfly algorithm enforced by the researcher.

关键词： mayfly algorithm Nuptial Dance Multiobjective mayfly algorithm Nature-inspired algorithm Unimodal Multimodal

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Android Malicious Application Detection Based on Improved mayfly algorithm 22

Android Malicious Application Detection Based on Improved Ma...

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IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) / BigDataSE Conference / CSE Conference / EUC Conference / ISCI Conference

作者： Wei, Yinzhen Lu, Shuo Wuhan Text Univ Sch Comp Sci & Artificial Intelligence Wuhan Peoples R China Wuhan Vocat Coll Software & Engn Huanggang Normal Coll Wuhan Peoples R China

ISBN: (纸本)9798350381993;9798350382006

With the rapid development of the Internet and mobile terminals, Android is one of the most popular mobile operating systems. However, the proliferation of Android malicious applications is also very serious, so it is necessary to detect and deal with Android applications in advance, and how to make effective selection among many features is a crucial process in malicious application detection. In this paper, an Android malicious application detection model is established based on the improved mayfly algorithm with reference to related Android malicious detection methods for Android platform applications. By effectively selecting the features, the optimal combination of features is obtained to optimize the classification results, so as to improve the detection performance of Android malicious application *** static analysis is used to extract the features of Android applications, and the malicious application detection model is examined by various classification algorithms, and the results confirm the feasibility and superiority of the proposed Android malicious application detection method based on the improved mayfly algorithm.

关键词： Malicious Application Detection mayfly algorithm Feature Selection Feature Optimization

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Improved mayfly algorithm Based on Co-Evolution for Feature Selection

Improved Mayfly Algorithm Based on Co-Evolution for Feature ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Tianyu, Guo Hongbin, Dong Jing, Zhou School of Computer Science and Technology Harbin Engineering University National Engineering Laboratory for E-government Modeling and Simulation Harbin China

ISBN: (纸本)9798350337020

mayfly algorithm (MA) is a new type of intelligent optimization algorithm with excellent search ability and a broad application prospect in feature selection. However, as the dimension of the data increases, MA also has the problem of weak search ability and falling into the local optimal solution. To solve these problems, this paper combines the idea of collaborative evolution and proposes an improved mayfly algorithm (CEIMA) suitable for feature selection problems. The CEIMA algorithm first divides the population into two sub-populations and initializes them in different ways to increase the diversity of the population. During evolution, information sharing is achieved through an information transmission mechanism between sub-populations, achieving collaborative evolution between sub-populations to enhance the global search ability of CEIMA. Finally, the mRMR-m strategy is proposed to mutate the global optimal individual based on feature importance, improving the ability of CEIMA to escape from local optimal solutions. Through a comparison with 7 feature selection algorithms on 12 UCI datasets, the effectiveness of CEIMA is proven. © 2023 IEEE.

关键词： co-evolution feature selection halton sequence max-relevance and min-redundancy mayfly algorithm

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An Improved Multi Objective mayfly algorithm for Solving Optimal Power Flow Problem Considering Different Loading Conditions

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2022年第10期31卷

作者： Ramesh, S. Bhaskar, K. Vijaya Karunanithi, K. Ettappan, M. Chandrasekar, P. Raja, S. P. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Elect & Elect Engn Chennai 600062 Tamil Nadu India Chennai Inst Technol Dept Elect & Elect Engn Chennai 600069 Tamil Nadu India Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

This paper presents an improved multi-objective mayfly algorithm (IMOMA) to resolve the optimal power flow (OPF) problem in a regulated power system network with different loading conditions. The OPF problem, considered a multi-objective optimization problem, comprises multiple objective functions related to economic, technical, operational and security aspects. The IMOMA algorithm has been developed by implementing the simulated binary crossover (SBX), polynomial mutation and dynamic crowding distance (DCD) operators in the original multi-objective mayfly algorithm (MOMA).The OPF problem is analyzed by considering multiple objective functions in the IEEE30-bus test system, the IEEE118-bus test system and the 62-bus Indian utility system. The hypervolume performance metric is used to compare the performance of the MOMA and IMOMA with respect to different operating scenarios. Further, loading conditions ranging between 150% and 50% of the base load are considered for the evaluation. The effectiveness of the IMOMA over the MOMA is observed from the results of the different loads. The best compromise solution is obtained from a set of pareto optimal solutions by implementing the TOPSIS method.

关键词： Hypervolume mayfly algorithm multi-objective optimization problem optimal power flow TOPSIS

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A parallel mayfly algorithm for the a-neighbor p-center problem

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APPLIED SOFT COMPUTING 2023年 144卷

作者： Cura, Tunchan Istanbul Univ Fac Business Adm Istanbul Turkiye

This study presents a competitive algorithm in which the mayfly optimization algorithm (MA) is combined with a local search procedure to provide high-quality solutions to the & alpha;-neighbor p-center problem (& alpha;- pCP). The aim of this problem is to locate p facilities on a plane such that the maximum distance between each demand point and its & alpha;th closest facility is minimized. Although & alpha;- pCP is a discrete optimization problem, it is addressed in this study from the perspective of a continuous optimization problem. The major reason for this choice is that the MA is a very suitable technique for finding the optimum point on a plane. However, it is very difficult to successfully apply this technique to a discrete optimization problem. Hence, additional efforts are made to reduce the computational burden that arises from approaching this from the perspective of a continuous problem. Thus, an effective solution method based on the MA for finding the optimum point on a plane is presented for the discrete & alpha;- pCP. The proposed MA method is compared to the best alternatives found in the literature, which are an extremely efficient exact procedure for the continuous variant of the problem, and a GRASP algorithm that includes a tabu search with strategic oscillation (SO) post-processing. A comparative analysis shows that the performance of the proposed algorithm is similar to that of these state-of-the-art methods. A more extensive comparison shows that the two best performing algorithms, the MA and the SO, complement each other. In addition, the proposed method performs very well for cases where the number of facilities are low. If we consider only the cases where p & LE;50, we see that the MA performs best 5 times, 14 times and 14 times for & alpha;= 1, 2, 3, respectively, and loses twice, three times, and three times.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Metaheuristics mayfly algorithm Local search Search-in-grid strategy p-center problem

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