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HSSAHHO: a novel hybrid Salp Swarm-Harris Hawks optimization algorithm for complex engineering problems

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第9期14卷 11569-11605页

作者： Singh, Narinder Houssein, Essam H. Singh, S. B. Dhiman, Gaurav Punjabi Univ Dept Math Patiala Punjab India Minia Univ Fac Comp & Informat El Minya Egypt Govt Bikram Coll Commerce Dept Comp Sci Patiala Punjab India

With metaheuristic algorithms hybridization is used typically to improve the performances of original algorithm, so it became a recent trend of research is to hybridize two and several number of variants to find out better quality of solution of practical and to tackle with recent real applications in the field of global optimization problems. Harris Hawks optimization (HHO) is powerful optimization tool that is robust, has smooth transitions between exploration and exploitation, that permits to provide competitive results in complex problems. Also, the Salp Swarm Algorithm (SSA) has been widely used to solve complex global optimization tasks due to its implementation simplicity and inexpensive computational overhead. However, SSA has premature convergence, is easily trapped in the local optimum solution and is ineffective in balancing exploration and exploitation. In this study, to overcome the shortcomings of HHO and SSA, a hybrid Salp Swarm Algorithm with Harris Hawks optimization (HHO) called (HSSAHHO) can be considered as a good choice for this purpose to solve these issues. Based on this consideration, hybrid SSAHHO is combination of SSA used for exploitation phase and HHO for exploration phase in uncertain environment. The movement directions and speed of the Salp' is improved using position update equations of HHO. To evaluate the effectiveness in solving the global optimization problems, HSSAHHO has been tested on 29-standard IEEE CEC 2017, 10-standard IEEE CEC 2020 benchmark test problems and also tested on six engineering design optimization problems. The numerical and statistical solutions obtained with HSSAHHO approach is compared with other meta-heuristics approaches such as Salp Swarm Optimizer (SSA), Particle Swarm optimization (PSO), Moth-flame optimization (MFO), Sine Cosine Algorithm (SCA), Enhanced grey wolf optimisation (EGWO), Augmented Grey Wolf Optimizer (AGWO) Hybrid Particle Swarm optimization-Grey Wolf Optimizer (PSOGWO), Harris Hawks Opti

关键词： Harris Hawks optimization metaheuristic optimization algorithms optimization Problems Salp Swarm Algorithm Swarm intelligence IEEE CEC-2017 and CEC-2020 benchmark functions

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Short-term photovoltaic power production forecasting based on novel hybrid data-driven models

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JOURNAL OF BIG DATA 2023年第1期10卷 26页

作者： Alrashidi, Musaed Rahman, Saifur Qassim Univ Coll Engn Dept Elect Engn Buraydah 52571 Saudi Arabia Virginia Tech Adv Res Inst Bradley Dept Elect & Comp Engn Arlington VA 22203 USA

The uncertainty associated with photovoltaic (PV) systems is one of the core obstacles that hinder their seamless integration into power systems. The fluctuation, which is influenced by the weather conditions, poses significant challenges to local energy management systems. Hence, the accuracy of PV power forecasting is very important, particularly in regions with high PV penetrations. This study addresses this issue by presenting a framework of novel forecasting methodologies based on hybrid data-driven models. The proposed forecasting models hybridize Support Vector Regression (SVR) and Artificial Neural Network (ANN) with different metaheuristic optimization algorithms, namely Social Spider optimization, Particle Swarm optimization, Cuckoo Search optimization, and Neural Network Algorithm. These optimization algorithms are utilized to improve the predictive efficacy of SVR and ANN, where the optimal selection of their hyperparameters and architectures plays a significant role in yielding precise forecasting outcomes. In addition, the proposed methodology aims to reduce the burden of random or manual estimation of such paraments and improve the robustness of the models that are subject to under and overfitting without proper tuning. The results of this study exhibit the superiority of the proposed models. The proposed SVR models show improvements compared to the default SVR models, with Root Mean Square Error between 12.001 and 50.079%. Therefore, the outcomes of this research work can uphold and support the ongoing efforts in developing accurate data-driven models for PV forecasting.

关键词： PV power forecast Machine learning metaheuristic optimization algorithms Hyperparameters and architectures tuning Feature selection

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Carbon capture power plants: Decoupled emission and generation outputs for economic dispatch

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INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL 2017年 63卷 12-19页

作者： Abdilahi, Abdirahman Mohamed Mustafa, Mohd Wazir Univ Teknol Malaysia Johor Baharu 81310 Johor Malaysia

Integrating flexibly-operated carbon capture and storage (CCS) into the existing power plants has operational benefits for the future low carbon power systems. This paper proposes an improved formulation for flexible operation of carbon capture power plants (CCPPs) within the conventional economic dispatch (ED) problem. The main contribution of this work is the simplification and the practicality of the variables used for the flexible operation control of the facility. The optimal ED problem of thermal power generation portfolio with CCPPs within the mix are computed using a chaos-enhanced Cuckoo Search optimization algorithm. To test the proposed formulations, an IEEE 30 bus test system was used. The impact of varying carbon prices on the system dispatch was investigated. The results reveal the potentiality of decoupling the generation and emission outputs of the thermal power plants.

关键词： Carbon capture and storage Economic dispatch Low carbon power systems metaheuristic optimization algorithms Power system operations Carbon pricing

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A novel explanatory hybrid artificial bee colony algorithm for numerical function optimization

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JOURNAL OF SUPERCOMPUTING 2020年第12期76卷 9330-9354页

作者： Jarrah, Muath Ibrahim Jaya, A. S. M. Alqattan, Zakaria N. Azam, Mohd Asyadi Abdullah, Rosni Jarrah, Hazim Abu-Khadrah, Ahmed Ismail Univ Tekn Malaysia Melaka Fac Informat & Commun Technol Durian Tunggal 76100 Melaka Malaysia Univ Sains Malaysia Sch Comp Sci Natl Adv IPv6 Ctr Gelugor 11800 Penang Malaysia Univ Tekn Malaysia Melaka Fac Mfg Engn Durian Tunggal 76100 Melaka Malaysia Auckland Univ Technol Dept Elect & Elect Engn Auckland New Zealand Saudi Elect Univ Coll Comp & Informat Riyadh Saudi Arabia

Over the past few decades, there has been a surge of interest of using swarm intelligence (SI) in computer-aided optimization. SI algorithms have demonstrated their efficacy in solving various types of real-world optimization problems. However, it is impossible to find an optimization algorithm that can obtain the global optimum for every optimization problem. Therefore, researchers extensively try to improve methods of solving complex optimization problems. Many SI search algorithms are widely applied to solve such problems. ABC is one of the most popular algorithms in solving different kinds of optimization problems. However, it has a weak local search performance where the equation of solution search in ABC performs good exploration, but poor exploitation. Besides, it has a fast convergence and can therefore be trapped in the local optima for some complex multimodal problems. In order to address such issues, this paper proposes a novel hybrid ABC with outstanding local search algorithm called beta-hill climbing (beta HC) and denoted by ABC-beta HC. The aim is to improve the exploitation mechanism of the standard ABC. The proposed algorithm was experimentally tested with parameters tuning process and validated using selected benchmark functions with different characteristics, and it was also evaluated and compared with well-known state-of-the-art algorithms. The evaluation process was investigated using different common measurement metrics. The result showed that the proposed ABC-beta HC had faster convergence in most benchmark functions and outperformed eight algorithms including the original ABC in terms of all the selected measurement metrics. For more validation, Wilcoxon's rank sum statistical test was conducted, and the p values were found to be mostly less than 0.05, which demonstrates that the superiority of the proposed ABC-beta HC is statistically significant.

关键词： metaheuristic optimization algorithms Swarm intelligence algorithms Artificial bee colony algorithm beta-Hill climbing Numerical benchmark function optimization

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Flood susceptibility modeling in the urban watershed of Guwahati using improved metaheuristic-based ensemble machine learning algorithms

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GEOCARTO INTERNATIONAL 2022年第26期37卷 12238-12266页

作者： Ahmed, Ishita Afreen Talukdar, Swapan Shahfahad Parvez, Ayesha Rihan, Mohd Baig, Mirza Razi Imam Rahman, Atiqur Jamia Millia Islamia Dept Geog Fac Nat Sci New Delhi India Univ Calif Irvine Henry Samueli Sch Engn Dept Elect Engn & Comp Sci Irvine CA USA

The urban watershed of Guwahati is a highly flood-prone region and the fastest growing city situated on the bank of the Brahmaputra River. Therefore, this study aims to the urban flood susceptibility mapping of Guwahati city using metaheuristic optimization algorithms integrated with random forest (RF) machine learning algorithm. Further, the receiver operating characteristic (ROC) and multiple error measurements were applied to analyze the performances of the models used. The result showed that about one-third of the area of Guwahati city is under the high and very high flood risk while nearly 50% area comes under low and very low flood risk. The value of the area under curve (AUC) of ROC was above 0.80 for all the integrated models applied. However, the RF-bee colony (BCO) and the RF-based ant colony (ACO) are the two best flood susceptibility models that performed better in the analysis. The methodology adopted in the study is cost and time effective and can be used for the flood susceptibility modeling in other parts of the world. Further, the findings of this study can useful in the flood mitigation and planning process.

关键词： Flood susceptibility mapping urban watershed metaheuristic optimization algorithms particle swarm optimization machine learning algorithms

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Enhanced GROMACS: toward a better numerical simulation framework

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JOURNAL OF MOLECULAR MODELING 2019年第12期25卷 1-8页

作者： Rakhshani, Hojjat Dehghanian, Effat Rahati, Amin Univ Upper Alsace Mulhouse France Univ Sistan & Baluchestan Dept Chem Zahedan Iran Univ Sistan & Baluchestan Dept Comp Sci Zahedan Iran

The GROMACS software package represented a promising direction toward the molecular dynamic simulation and there is ongoing interest to extend it. In this study, we introduce a new component into the conventional package with the goal being to facilitate the process of finding the native structure of proteins with minimal free-energy value. We achieved this through incorporating a wide range of metaheuristic optimization algorithms and force fields, leading up to the EGROMACS molecular simulation toolkit. Compared with other programs, the EGROMACS supports all standard force fields as well as new minimization algorithms and Hybrid MPI/OpenMP parallelization. We applied the proposed EGROMACS framework to minimize the structure of several target sequences. The obtained results showed comparative performance of the introduced framework to current well-known molecular simulation algorithms. This extension to the GROMACS, however, uses metaheuristic algorithms to address the problem.

关键词： GROMACS software package metaheuristic optimization algorithms Molecular dynamic simulation Structure of proteins

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A Multi-objective Genetic Local Search Algorithm for Optimal Feature Subset Selection

A Multi-objective Genetic Local Search Algorithm for Optimal...

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International Conference on Computational Science and Computational Intelligence (CSIC)

作者： Tian, David Leeds Beckett Univ Leeds Sustainabil Inst Leeds LS1 3HE W Yorkshire England

ISBN: (纸本)9781509055104

Feature selection algorithms select the most relevant features of a data set to improve the classification performance of the machine learning classifiers trained using the data set. This paper proposes a feature selection algorithm called multi-objective genetic local search (MOGLS) which integrates a 3-objective genetic algorithm with a local search heuristic to find feature subsets with the maximum prediction accuracy, the smallest sizes and the minimum redundancy. The performance of MOGLS is compared with 4 algorithms: a wrapper genetic algorithm, correlation-based feature selection, mutual information ranking and C4.5 on 8 datasets from the UCI machine learning repository. MOGLS performs better than or as good as the 4 algorithms on the 8 datasets.

关键词： Machine learning technologies Multi-objective evolutionary algorithms Genetic algorithms metaheuristic optimization algorithms Fuzzy and rough data analysis

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Wireless Sensor Network Coverage Problem Using Modified Fireworks Algorithm 12

Wireless Sensor Network Coverage Problem Using Modified Fire...

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12th International Wireless Communications & Mobile Computing Conference (IWCMC)

作者： Tuba, Eva Tuba, Milan Simian, Dana Univ Belgrade Fac Math Belgrade Serbia John Naisbitt Univ Fac Comp Sci Belgrade Serbia Lucian Blaga Univ Fac Sci Sibiu Romania

ISBN: (纸本)9781509003044

Wireless sensor networks are emerging technology with increasing number of applications, and consequently an active research area. One of the problems pertinent to wireless sensor networks is the coverage problem with number of definitions, depending on the assumed conditions. In this paper we consider hard optimization area coverage problem with the goal of finding optimal sensor nodes positions that maximize probabilistic coverage of the area of interest. For such type of optimization problem swarm intelligence stochastic metaheuristics have been successfully used. In this paper we propose a modified enhanced fireworks algorithm for wireless sensor network coverage problem and compare it with other approaches from literature, where our algorithm proved to be very robust and better, considering all conducted tests.

关键词： Wireless sensor networks Coverage problem Fireworks algorithm Swarm intelligence metaheuristic optimization algorithms

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Improved Whale optimization Algorithm for SVM Model Selection: Application in Medical Diagnosis 28

Improved Whale Optimization Algorithm for SVM Model Selectio...

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28th International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

作者： Ben Chaabane, Sarra Kharbech, Sofiane Belazi, Akram Bouallegue, Ammar Tunis El Manar Univ Lab SysCom ENIT LR 99 ES21 Tunis 1002 Tunisia Univ Lille Lab IEMN IRCICA UMR CNRS 8520 F-59100 Lille France Tunis El Manar Univ Lab RISC ENIT LR 16 ES07 Tunis 1002 Tunisia

ISBN: (纸本)9789532900996

Support-Vector Machines (SVM) have gained momentum and have become a widely used classifier for various applications. Indeed, SVM parameters have a significant impact on the accuracy of the prediction model. The present work aims to propose an improved version of the Whale optimization Algorithm (WOA) that can choose the best model for SVM by seeking the optimal parameter values. To surpass the premature convergence problem and outperform the exploitation ability of the original WOA, two improvements are introduced. First, we increase the potential of the best solution, which redirects the search agent toward the global optimum. Second, a new search equation is added to decrease the overflow of diversity. Experimental results proved that the proposed Improved Whale optimization Algorithm (IWOA) is efficient not only in terms of quality of the final solution and convergence rate for optimization benchmarking function but also for reaching the optimal values of the SVM parameters. In this sense, the study has provided a medical diagnosis-oriented problem, classifying medical, i.e., cancer and diabetes, datasets, to demonstrate the effectiveness of the IWOA-optimized SVM.

关键词： metaheuristic optimization algorithms machine learning support-vector machines classification model selection

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Modified Krill Herd optimization Algorithm using Chaotic Parameters 4

Modified Krill Herd Optimization Algorithm using Chaotic Par...

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4th International Conference on Computer and Knowledge Engineering (ICCKE)

作者： Bidar, Mandi Fattahi, Edris Kanan, Hamidreza Rashidy Islamic Azad Univ Dept Elect Comp & IT Engn Qazvin Branch Qazvin Iran Bu Ali Siria Univ Dept Elect Engn Hamadan Iran

ISBN: (纸本)9781479954865

Krill Herd optimization algorithm which is a new metaheuristic search algorithm mimics the herding behavior of krill individuals. The significant characteristic of meta heuristic algorithms is their ability in combination of local search and global search. This property can adjust the contribution of local search and global search in initial step and during searching process and plays crucial role in the algorithm performance. One hazard which threats the meta heuristic algorithms is getting stuck in local optimum traps. An appropriate solution to deal with this problem is using chaos theory which brings dynamism and instability properties to the algorithm so that by strengthening the performance of random search helps the algorithm to escape from local optimum traps. In this paper, we propose a new method called chaotic Krill Herd optimization algorithm which by adopting chaos theory in Krill Herd optimization algorithm heighten its performance in dealing with various optimization problems. The obtained results by the proposed method in comparison with those of the standard Krill Herd optimization algorithm indicates the higher performance of the proposed algorithm.

关键词： metaheuristic optimization algorithms Krill Herd optimization Algorithm Chaotic Krill Herd optimization Algorithm Exploration Exploitation

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