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artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm

NEURAL COMPUTING & APPLICATIONS 2020年第13期32卷 9383-9425页

作者： Zhao, Weiguo Wang, Liying Zhang, Zhenxing Hebei Univ Engn Sch Water Conservancy & Hydropower Handan 056021 Hebei Peoples R China Univ Illinois Prairie Res Inst Illinois State Water Survey Champaign IL 61820 USA

A novel nature-inspired meta-heuristic optimization algorithm, named artificial ecosystem-based optimization (AEO), is presented in this paper. AEO is a population-based optimizer motivated from the flow of energy in an ecosystem on the earth, and this algorithm mimics three unique behaviors of living organisms, including production, consumption, and decomposition. AEO is tested on thirty-one mathematical benchmark functions and eight real-world engineering design problems. The overall comparisons suggest that the optimization performance of AEO outperforms that of other state-of-the-art counterparts. Especially for real-world engineering problems, AEO is more competitive than other reported methods in terms of both convergence rate and computational efforts. The applications of AEO to the field of identification of hydrogeological parameters are also considered in this study to further evaluate its effectiveness in practice, demonstrating its potential in tackling challenging problems with difficulty and unknown search space. The codes are available at.

关键词： artificial ecosystem-based optimization Global optimization Constrained problems optimization algorithm Engineering design Hydrogeological parameter

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artificial ecosystem-based optimization with Dwarf Mongoose optimization for Feature Selection and Global optimization Problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2023年第1期16卷 102-102页

作者： Al-Shourbaji, Ibrahim Kachare, Pramod Fadlelseed, Sajid Jabbari, Abdoh Hussien, Abdelazim G. Al-Saqqar, Faisal Abualigah, Laith Alameen, Abdalla Jazan Univ Dept Comp & Network Engn Jazan 45142 Saudi Arabia Univ Hertfordshire Dept Comp Sci Hatfield AL10 9AB England Ramrao Adik Inst Technol Dept Elect & Telecomm EngineeringNerul Navi Mumbai 400706 Maharashtra India Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Fayoum Univ Fac Sci Faiyum Egypt Al Al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Comp Sci Dept Mafraq 25113 Jordan Sunway Univ Malaysia Sch Engn & Technol Petaling Jaya 27500 Malaysia Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ Fac Informat Technol Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Prince Sattam Bin Abdulaziz Univ Coll Arts & Sci Dept Comp Sci Al Kharj Saudi Arabia

Meta-Heuristic (MH) algorithms have recently proven successful in a broad range of applications because of their strong capabilities in picking the optimal features and removing redundant and irrelevant features. artificial ecosystem-based optimization (AEO) shows extraordinary ability in the exploration stage and poor exploitation because of its stochastic nature. Dwarf Mongoose optimization Algorithm (DMOA) is a recent MH algorithm showing a high exploitation capability. This paper proposes AEO-DMOA Feature Selection (FS) by integrating AEO and DMOA to develop an efficient FS algorithm with a better equilibrium between exploration and exploitation. The performance of the AEO-DMOA is investigated on seven datasets from different domains and a collection of twenty-eight global optimization functions, eighteen CEC2017, and ten CEC2019 benchmark functions. Comparative study and statistical analysis demonstrate that AEO-DMOA gives competitive results and is statistically significant compared to other popular MH approaches. The benchmark function results also indicate enhanced performance in high-dimensional search space.

关键词： Feature selection Machine learning Metaheuristic algorithms artificial ecosystem-based optimization Dwarf mongoose optimization

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Discrete artificial ecosystem-based optimization for spherical capacitated vehicle routing problem

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第13期83卷 37315-37350页

作者： Tang, Jiaju Luo, Qifang Zhou, Yongquan Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi Univ Nationalities Xiangsihu Coll Nanning 530225 Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

The capacity constrained vehicle routing problem (CVRP) is one of the most well-known combinatorial optimization problems. Many studies have proposed various methods to solve the two-dimensional CVRP problem. In this paper, we firstly extend the CVRP problem to a three-dimensional space, called SCVRP, where all customers are distributed on a sphere. Secondly, a discrete artificial ecosystem-based optimization algorithm (DAEO) is proposed to solve the SCVRP problem. DAEO uses five local search operators to discretize the position update formula of the original AEO algorithm and introduces the 2-opt algorithm to simulate the mutation mechanism of organisms in the ecosystem. Furthermore, to evaluate the performance of the proposed algorithm, 32 test instances are designed, having 25-400 customers. These instances take into account factors such as customer position and demand distribution to be closer to the actual situation. The experimental results show that the proposed algorithm has good feasibility and competitiveness in solving the SCVRP problem, and can obtain better solutions than other intelligent algorithms.

关键词： Capacitated vehicle routing problem artificial ecosystem-based optimization Local search operators 2-opt algorithm Spherical CVRP

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A novel metaheuristic method based on artificial ecosystem-based optimization for optimization of network reconfiguration to reduce power loss

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SOFT COMPUTING 2021年第23期25卷 14729-14740页

作者： Nguyen, Thuan Thanh Ind Univ Ho Chi Minh City Fac Elect Engn Technol Ho Chi Minh City Vietnam

Power loss reduction is a very important problem in operating the distribution network. This paper presents a method relied on artificial ecosystem-based optimization (AEO) for the network reconfiguration (NR) problem. The considered goal of the NR problem is to reduce power loss. The performance of the proposed AEO method is evaluated on the 14-node, 69-node and 136-node distribution networks. In addition, the NR approach relied on cuckoo search algorithm (CSA) is also implemented to compare with the proposed AEO method. The statistics result comparisons between AEO and CSA demonstrate that AEO outperforms CSA method with the higher success rate and better quality of the obtained solution in finding the optimal network configuration. In addition, AEO is also better than some previous approaches in the literature in term of the obtained network configuration. Consequently, AEO method is a very effective approach for the NR problem.

关键词： Power loss reduction artificial ecosystem-based optimization Cuckoo search algorithm Network reconfiguration

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Developing Chaotic artificial ecosystem-based optimization Algorithm for Combined Economic Emission Dispatch

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IEEE ACCESS 2021年 9卷 51146-51165页

作者： Hassan, Mohamed H. Kamel, Salah Salih, Sinan Q. Khurshaid, Tahir Ebeed, Mohamed Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Dijlah Univ Coll Comp Sci Dept Baghdad 00964 Iraq Yeungnam Univ Dept Elect Engn Gyongsan 38541 South Korea Sohag Univ Fac Engn Dept Elect Engn Sohag 82524 Egypt

In this paper, Chaotic artificial ecosystem-based optimization Algorithm (CAEO) is proposed and utilized to determine the optimal solution which achieves the economical operation of the electrical power system and reducing the environmental pollution produced by the conventional power generation. Here, the Combined Economic Emission Dispatch (CEED) problem is represented using a max/max Price Penalty Factor (PPF) to confine the system's nonlinearity. PPF is considered to transform a four-objective problem into a single-objective optimization problem. The proposed modification of AEO raises the effectiveness of the populations to achieve the best fitness solution by well-known 10 chaotic functions and this is valuable in both cases of the single and multi-objective functions. The CAEO algorithm is used for minimizing the economic load dispatch and the three bad gas emissions which are sulfur dioxide (SO2), nitrous oxide (NOx), and carbon dioxide (CO2). To evaluate the proposed CAEO, it is utilized for four different levels of demand in a 6-unit power generation (30-bus test system) and 11-unit power generation (69-bus test system) with a different value of load demand (1000, 1500, 2000, and 2500MW). Statistical analysis is executed to estimate the reliability and stability of the proposed CAEO method. The results obtained by CAEO algorithm are compared with other methods and conventional AEO to prove that the modification is to boost the search strength of conventional AEO. The results display that the CAEO algorithm is superior to the conventional AEO and the others in achieving the best solution to the problem of CEED in terms of efficient results, strength, and computational capability all over study cases. In the second scenario of the bi-objective problem, the Pareto theory is integrated with a CAEO to get a series of Non-Dominated (ND) solutions, and then using the fuzzy approach to determine BCS.

关键词： optimization Power generation Economics Fuels Genetic algorithms Reliability Licenses Combined economic and emission dispatch artificial ecosystem-based optimization greenhouse gases Pareto front price penalty factor chaotic AEO

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A new artificial ecosystem-based optimization integrated with Nelder-Mead method for PID controller design of buck converter

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ALEXANDRIA ENGINEERING JOURNAL 2022年第3期61卷 2030-2044页

作者： Izci, Davut Hekimoglu, Baran Ekinci, Serdar Batman Univ Dept Elect & Automat TR-72060 Batman Turkey Batman Univ Dept Elect & Elect Engn TR-72060 Batman Turkey Batman Univ Dept Comp Engn TR-72060 Batman Turkey

Over the last decade, there has been a constant development in control techniques for DC-DC power converters which can be classified as linear and nonlinear. Researchers focus on obtaining maximum efficiency using linear control techniques to avoid complexity although nonlinear control techniques may achieve full dynamic capabilities of the converter. This paper has a similar purpose in which a novel hybrid metaheuristic optimization algorithm (AEONM) is proposed to design an optimal PID controller for DC-DC buck converter's output voltage regulation. The AEONM employs artificial ecosystem-based optimization (AEO) algorithm with Nelder-Mead (NM) simplex method to ensure optimal PID controller parameters are efficiently tuned to control output voltage of the buck converter. Initial evaluations are performed on benchmark functions. Then, the performance of AEONM-based PID is validated through comparative results of statistical boxplot, non-parametric test, transient response, frequency response, time-domain integralerror-performance indices, disturbance rejection and robustness using AEO, particle swarm optimization and differential evolution. A comparative performance analysis of transient and frequency responses is also performed against simulated annealing, whale optimization and genetic algorithms for further performance assessment. The comparisons have shown the proposed hybrid AEONM algorithm to be superior in terms of enhancing the buck converter's transient and frequency responses. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.

关键词： artificial ecosystem-based optimization Nelder-Mead method PID controller Buck converter

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Enhanced artificial ecosystem-based optimization for global optimization and constrained engineering problems

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第7期27卷 10053-10092页

作者： Wang, Yunpeng Zhang, Jixiang Zhang, Mengjian Wang, Deguang Yang, Ming Guizhou Univ Sch Elect Engn Guiyang 550025 Guizhou Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China

artificial ecosystem-based optimization (AEO) is a nature-inspired intelligent optimization algorithm that has been widely applied to various real-world optimization problems. However, AEO has several limitations, including slow convergence and difficulty in escaping from local optima. To address these drawbacks, this study proposes an enhanced variant of AEO called enhanced artificial ecosystem-based optimization (EAEO). First, Latin hypercube sampling is introduced to achieve uniform population initialization. Then, a quadratic interpolation mechanism is embedded to accelerate convergence and improve accuracy. Finally, an adaptive neighborhood search inspired by animal migration behavior is designed to help to jump out of local optima. The performance of EAEO is evaluated using twenty-three benchmark functions and the CEC2017 test suite. The impact analysis, statistical analysis, and sensitivity analysis are performed. Experimental results indicate that EAEO outperforms the original AEO and other comparison algorithms in terms of accuracy and stability. Finally, the proposed EAEO is applied to address seven engineering optimization problems, and the results demonstrate the superiority of EAEO for global optimization tasks, constrained engineering problems, search performance, solution accuracy, and convergence speed.

关键词： artificial ecosystem-based optimization Latin hypercube sampling Quadratic interpolation Adaptive neighborhood search Global optimization Constrained engineering problems

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MOAEOSCA: an enhanced multi-objective hybrid artificial ecosystem-based optimization with sine cosine algorithm for feature selection in botnet detection in IoT

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第9期82卷 13369-13399页

作者： Hosseini, Fatemeh Gharehchopogh, Farhad Soleimanian Masdari, Mohammad Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran

The number of Internet of Things (IoT) devices overgrows, and this technology dominates. The importance of IoT security and the growing need to devise intrusion detection systems (IDSs) to detect all types of attacks. The limited sources on the IoT. They have led researchers to explore and provide new and efficient solutions to create Botnet Detection in IoT systems. These systems use data features to detect network traffic status and thus detect malicious behavior. Also, data set features indicate the type of network traffic. Many features in the problem space and network behaviour unpredictability make IDSs the main challenge in establishing security in computer networks. Many unnecessary features have also made feature selection an essential aspect of attack detection systems. This paper developed a multi-objective MOAEOSCA algorithm hybridizing artificial ecosystem-based optimization (AEO) algorithms and the Sine Cosine Algorithm (SCA) for botnet detection in IoT. By accurately identifying the weaknesses of the MOAEOSCA algorithm, it has been tried to cover the weaknesses to a large extent and to reach a robust algorithm. We promoted the proposed algorithm using Bitwise operations, Disruption operator, and Opposition-based learning (OBL) mechanisms. Ten standard datasets in the UCI repository were examined to evaluate the proposed algorithm's performance in solving the feature selection problem to detect a botnet. Simulation findings indicated that the proposed algorithm had an acceptable accuracy in Botnet Detection in the IoT, outperforming other methods. According to the experiments carried out in this paper, the MOAEOSCA algorithm has shown that nine data sets out of ten data sets in the feature selection problem performed better than other optimization algorithms. But in all seven botnet data sets, performance has shown better than different optimization algorithms.

关键词： Multi-objective artificial ecosystem-based optimization Sine cosine algorithm Feature selection Botnet detection in IoT

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Prediction of power consumption and water productivity of seawater greenhouse system using random vector functional link network integrated with artificial ecosystem-based optimization

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2020年 144卷 322-329页

作者： Essa, F. A. Abd Elaziz, Mohamed Elsheikh, Ammar H. Kafrelsheikh Univ Fac Engn Mech Engn Dept Kafrelsheikh 33516 Egypt Zagazig Univ Fac Sci Dept Math Zagazig Egypt Tanta Univ Dept Prod Engn & Mech Design Tanta 31527 Egypt

The seawater greenhouse desalination technology is a kind of desalination plant which simulates the water cycle through seawater evaporation and condensation into freshwater. A novel random vector functional link (RVFL) network integrated with artificial ecosystem-based optimization (AEO) algorithm is proposed to predict the performance of the seawater greenhouse (SWGH) system. Power consumption and water productivity of the SWGH are predicted using the proposed RVFL-AEO model. The statistical analyses using different statistical criteria such as root mean square error, mean absolute error, mean relative error, efficiency coefficient, coefficient of determination, overall index, and coefficient of residual mass are also carried out to examine the efficiency of the proposed neural network. The statistical tools obtained a perfect match between the experimental and the proposed model results. The performance of the RVFL-AEO model is compared with that of the conventional RVFL model. RVFL-AEO showed a better performance compared with RVFL;which indicates the role of AEO in obtaining the optimal RVFL parameters that enhances the accuracy of the model. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Seawater greenhouse artificial neural network Desalination Random vector functional link artificial ecosystem-based optimization

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Hybrid artificial ecosystem-based optimization Algorithm Solves Photovoltaic Models Parameter Identification Problem 14th

Hybrid Artificial Ecosystem-Based Optimization Algorithm Sol...

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14th International Conference on Swarm Intelligence (ICSI)

作者： Yu, Xiaoqiu Zhengzhou Univ Light Ind Coll Comp & Commun Engn Zhengzhou Peoples R China

ISBN: (纸本)9783031366215;9783031366222

Photovoltaic (PV) models have both nonlinear and multimodal characteristics, and traditional algorithms are prone to fall into local optimum and insufficient recognition accuracy in identifying their parameters. This paper proposes a hybrid artificial ecosystem-based optimization (HAEO) algorithm for fast, accurate, and reliable identification of PV model parameters under different environmental conditions. In HAEO, aNelder-Mead polyhedral search strategy is used to update the poorer individuals. In addition, a chaotic local search strategy is introduced to enhance the ability of populations to jump out of the local optimum by using the ergodic and non-repetitive nature of chaos. To verify the effectiveness of the proposed algorithm, it is used for the parameter identification of thin-film ST40 PV modules under different irradiation intensities and temperatures conditions. Simulation results show that the HAEO algorithm is more competitive than the existing algorithms in terms of accuracy and reliability. Therefore, HAEO can be considered an effective PV model parameter identification method.

关键词： PV model Parameter identification artificial ecosystem-based optimization

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