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Optimal operation of the multi-reservoir system: a comparative study of robust metaheuristic algorithms

INTERNATIONAL JOURNAL OF HYDROLOGY SCIENCE AND TECHNOLOGY 2024年第3期17卷 239-266页

作者： Verma, Shashikant Prasad, A. D. Verma, Mani Kant Natl Inst Technol Raipur Dept Civil Engn Great Eastern Rd Raipur 492010 Chhattisgarh India

In a multi-reservoir system, the functioning of one reservoir affects the other reservoirs. A multi-reservoir system must be managed as a single entity for sustainable water management. Thus, assessing a multi-reservoir system's existing operating policy is crucial for integrated operation. In this study, the recently introduced grey wolf optimisation (GWO) was compared to the robust metaheuristic algorithm the whale optimisation algorithm (WOA). First, the performance of these algorithms was evaluated and compared for multi-reservoir system operation optimisation. The statistical indices coefficient of determination (R-2), root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), normalise mean square error (NMSE), and mean absolute percentage error (MAPE) were used to compare the algorithms' performance. According to the test findings, among the ten algorithms evaluated. GWO was shown to be the most effective, and it is suggested as a reliable and promising technique for optimising multi-reservoir systems.

关键词： metaheuristic algorithm multi-reservoir system Mahanadi reservoir project grey wolf optimisation GWO whale optimisation algorithm WOA

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Skill Optimization algorithm:A New Human-Based metaheuristic Technique

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Computers, Materials & Continua 2023年第1期74卷 179-202页

作者： Hadi Givi Marie Hubalovska Department of Electrical Engineering Shahreza CampusUniversity of IsfahanIran Department of Technics Faculty of EducationUniversity of Hradec KraloveCzech Republic

metaheuristic algorithms are widely used in solving optimization *** this paper,a new metaheuristic algorithm called Skill Optimization algorithm(SOA)is proposed to solve optimization *** fundamental inspiration in designing SOA is human efforts to acquire and improve *** stages of SOA are mathematically modeled in two phases,including:(i)exploration,skill acquisition from experts and(ii)exploitation,skill improvement based on practice and individual *** efficiency of SOA in optimization applications is analyzed through testing this algorithm on a set of twenty-three standard benchmark functions of a variety of unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** optimization results show that SOA,by balancing exploration and exploitation,is able to provide good performance and appropriate solutions for optimization *** addition,the performance of SOA in optimization is compared with ten metaheuristic algorithms to evaluate the quality of the results obtained by the proposed *** and comparison of the obtained simulation results show that the proposed SOA has a superior performance over the considered algorithms and achievesmuch more competitive results.

关键词： Optimization human-based skill exploration exploitation metaheuristic algorithm

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Billiards Optimization algorithm:A New Game-Based metaheuristic Approach

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Computers, Materials & Continua 2023年第3期74卷 5283-5300页

作者： Hadi Givi Marie Hubálovská Department of Electrical Engineering Shahreza CampusUniversity of IsfahanIran Department of Technics Faculty of EducationUniversity of Hradec KraloveCzech Republic

metaheuristic algorithms are one of themost widely used stochastic approaches in solving optimization *** this paper,a new metaheuristic algorithm entitled Billiards Optimization algorithm(BOA)is proposed and designed to be used in optimization *** fundamental inspiration in BOA design is the behavior of the players and the rules of the billiards *** steps of BOA are described and then its mathematical model is thoroughly *** efficiency of BOA in dealing with optimization problems is evaluated through optimizing twenty-three standard benchmark functions of different types including unimodal,high-dimensional multimodal,and fixed-dimensionalmultimodal *** order to analyze the quality of the results obtained by BOA,the performance of the proposed approach is compared with ten well-known *** simulation results show that BOA,with its high exploration and exploitation abilities,achieves an impressive performance in providing solutions to objective functions and is superior and far more competitive compared to the ten competitor algorithms.

关键词： Optimization game-based billiards game exploration exploitation metaheuristic algorithm

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metaheuristic algorithms for Multiagent Routing Problems

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AUTOMATION AND REMOTE CONTROL 2021年第10期82卷 1787-1801页

作者： Germanchuk, M. S. Lemtyuzhnikova, D., V Lukianenko, V. A. Vernadsky Crimean Fed Univ Simferopol 295007 Russia Russian Acad Sci Trapeznikov Inst Control Sci Moscow 117997 Russia Moscow Inst Aviat Technol Moscow 125993 Russia

The problems of constructing routes in complex networks by many sales agents are considered. Formalization leads to pseudo-Boolean discrete optimization problems with constraints that take into account the specifics of route construction. The sparsity of the constraint matrix permits one to apply decomposition approaches and network clustering. The development of approximate algorithms for selecting routes in complex networks involves taking into account the properties of the network structure, its complexity, the presence of restrictions, regulations, reachability conditions, and the number of sales agents. It is shown that the solution of routing problems can be based on the application of a multiagent approach in combination with clustering (decomposition) of the original problem and metaheuristics. Multiagent systems with swarm intelligence are used to solve complex discrete optimization problems that cannot efficiently be solved by classical algorithms. The agent model for a complex network of a problem of the many traveling salesmen type becomes an intelligent system that defines heuristic algorithms for finding the optimal solution by reactive agents (that follow the rules laid down in them). We use and describe in detail the composition of the algorithms, which have proven themselves well in computational experiments;those are a modification of the genetic algorithm, ant colony optimization, artificial bee colony algorithm, and simulated annealing. A generalized algorithm is proposed and implemented in which a simpler network (a flyover network) is matched to the source network. Here a numerical experiment was carried out for the problem of routing on a GIS map for urban infrastructure. Clustering algorithms are implemented in which the initially traversed routes are refined using 2-opt algorithms, simulated annealing, and other metaheuristics. A comparison of the algorithms used and an illustration of their operation are given.

关键词： metaheuristic algorithm multiagent optimization problem discrete optimization pseudo-Boolean problem

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Comparison of metaheuristic algorithms for Photovoltaic Systems Allocation in a Power Distribution Feeder 2

Comparison of Metaheuristic Algorithms for Photovoltaic Syst...

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2nd IEEE Conference on Artificial Intelligence (CAI)

作者： Jaramillo-Leon, Brian Almeida, Jose Soares, Joao Leite, Jonatas B. Zambrano-Asanza, Sergio Vale, Zita Sao Paulo State Univ Dept Elect Engn Sao Paulo Brazil Polytech Porto GECAD LASI Porto Portugal Sao Paulo State Univ CENTROSUR Dept Planning Dept Elect Engn Cuenca Ecuador

ISBN: (纸本)9798350354102;9798350354096

The government's endorsement of renewable energy objectives and the requirement to use carbon-free energy sources to keep up with the growth in energy consumption have expanded the integration of solar photovoltaic (PV) systems in distribution networks. However, an excessive PV penetration may lead to operational threshold violations. PV system allocation that is optimal in terms of placement and sizing can enhance power quality and grid performance. We formulate the allocation of PV systems as a combinatorial mixed-integer nonlinear model to maximize the distribution network PV hosting capacity (PVHC). We chose three differential evolution (DE) mutation strategies, namely DE/rand/1/bin, DE/***/1/bin, and DE/rand/1/***, and the vortex search (VS) algorithm to solve that optimization problem. This study aims to identify the method that solves the PV allocation problem with higher quality. We performed manual parameter tuning to set both the population and iteration numbers for each algorithm. In addition, for the DE mutation strategies, we set the scale factor and crossover rate parameters. The results show that the VS provides the highest grid PVHC.

关键词： differential evolution distribution system hosting capacity metaheuristic algorithm photovoltaic allocation vortex search

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metaheuristic algorithms applied in ANN salinity modelling

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RESULTS IN ENGINEERING 2024年 23卷

作者： Khudhair, Zahraa S. Zubaidi, Salah L. Dulaimi, Anmar Al-Bugharbee, Hussein Muhsen, Yousif Raad Jaya, Ramadhansyah Putra Ridha, Hussein Mohammed Raza, Syed Fawad Ethaib, Saleem Wasit Univ Dept Civil Engn Wasit 52001 Iraq Univ Warith Al Anbiyaa Coll Engn Karbala 56001 Iraq Univ Kerbala Coll Engn Karbala 56001 Iraq Liverpool John Moores Univ Sch Civil Engn & Built Environm Liverpool L3 2ET England Wasit Univ Dept Mech Engn Wasit 52001 Iraq Al Ayen Univ Tech Engn Coll Thi Qar 64001 Iraq Univ Malaysia Pahang Al Sultan Abdullah Fac Civil Engn Technol Kuantan 26300 Pahang Malaysia Univ Al Mustansiriyah Dept Comp Engn Baghdad 10001 Iraq Univ Punjab Coll Earth & Environm Sci Lahore Pakistan Univ Thi Qar Coll Engn Dept Civil Engn Al Nassiriya 64001 Iraq

Salinity is a classic problem in planning the quality of freshwater resources management. Recent studies related to hybrid machine learning models have shown it's capability to simulate salinity dynamics. However, previous studies of metaheuristic algorithms have not dealt with comparing single- and hybrid-based algorithms in much detail. The present study aimed to develop univariate salinity by applying an artificial neural network model (ANN) integrated with (hybrid-based) coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA). The methodology was developed and tested using electrical conductivity (EC) and total dissolved solids (TDS) data collected from the Euphrates River in Babylon Province, Iraq, from 2010 to 2019. The CPSOCGSA performance was evaluated by various single-based ones, including multi-verse optimiser (MVO), marine predator's optimisation algorithm (MPA), particle swarm optimiser (PSO), and the slim mould algorithm (SMA). The principal finding here confirms that hybrid-based outperformed four single-based algorithms based on different criteria. The outcomes for TDS were 0.004, 0.0248, and 0.98 for CPSOCGSA-ANN technique concern scatter index (SI), root-mean-squared error (RMSE), and correlation coefficient (R 2 ), respectively. For EC, the results were 0.96 for R 2 , 0.0386 for RMSE, and 0.006 for SI. Due to its predictive accuracy, the proposed CPSOCGSA-ANN approach is suggested as a potential strategy for predicting monthly salinity data. Considering agriculture's vital role in Babylon Province's economy, this study may help inform future freshwater quality management decisions.

关键词： Artificial neural network metaheuristic algorithm Data pre-processing Surface water quality Euphrates river

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Investigation of green production inventory problem with selling price and green level sensitive interval-valued demand via different metaheuristic algorithms

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SOFT COMPUTING 2022年第19期26卷 10409-10421页

作者： Manna, Amalesh Kumar Bhunia, Asoke Kumar Univ Burdwan Dept Math Burdwan 713104 W Bengal India CV Raman Global Univ Dept Math Bhubaneswar 752054 India

In real-life situation, different characteristics of an imperfect production inventory system, viz. demand, production, defectiveness and different costs may be imprecise. To represent the impreciseness of those parameters, several well-known approaches like interval, fuzzy, stochastic and fuzzy-stochastic approaches are applied. Among these approaches, interval approach is more realistic. This work deals with a green production model in interval environment. Due to consideration of interval-valued demand and defective rates, the corresponding differential equations of inventory levels of the proposed model are represented in interval form. With the help of parametric representation of interval, these differential equations are solved. The corresponding profit maximization problem is obtained in the interval form. To transform the said maximization problem into crisp form, interval order relations and centre-radius optimization technique are used and, the transformed problem is solved with the help of different metaheuristic algorithms. Then the proposed model is justified and validated with the help of a numerical example. Finally, the sensitivity analyses are performed to explore the effects of changes of different system parameters on the best found (optimal) policy.

关键词： Interval-valued demand Green product Interval differential equation Center-radius optimization technique metaheuristic algorithm

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Reactions’Descriptors Selection and Yield Estimation Using metaheuristic algorithms and Voting Ensemble

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Computers, Materials & Continua 2022年第3期70卷 4745-4762页

作者： Olutomilayo Olayemi Petinrin Faisal Saeed Xiangtao Li Fahad Ghabban Ka-Chun Wong Department of Computer Science City University of Hong KongKowloon TongHong Kong SAR Information Systems Department College of Computer Science and EngineeringTaibah UniversityTaybaMedina42353Saudi Arabia Hong Kong Institute for Data Science City University of Hong KongKowloon TongHong Kong SAR

Bioactive compounds in plants,which can be synthesized using N-arylationmethods such as the Buchwald-Hartwig reaction,are essential in drug discovery for their pharmacological *** descriptors are necessary for the estimation of yields in these *** study explores ten metaheuristic algorithms for descriptor selection and model a voting ensemble for *** algorithms were evaluated based on computational time and the number of selected *** show that robust performance is obtained with more descriptors,compared to cases where fewer descriptors are *** essential descriptor was deduced based on the frequency of occurrence within the 50 extracted data subsets,and better performance was achieved with the voting ensemble than other algorithms with RMSE of 6.4270 and R^(2) of *** results and deductions from this study can be readily applied in the decision-making process of chemical synthesis by saving the computational cost associated with initial descriptor selection for yield *** ensemble model has also shown robust performance in its yield estimation ability and efficiency.

关键词： Buchwald-Hartwig reaction descriptor selection machine learning metaheuristic algorithm palladium-catalyzed cross-coupling reaction voting ensemble

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A Comparative Study of metaheuristic algorithms for Financial Portfolio Optimization: Evaluating Performance and Efficiency 15

A Comparative Study of Metaheuristic Algorithms for Financia...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Gupta, Umesh Jain, Vasu Jain, Dev Bennett University SCSET Greater Noida India Bennett University SOM Greater Noida India

ISBN: (纸本)9798350370249

This paper investigates a comparison of different meta-heuristic algorithms in the optimization of financial portfolios that have the highest yield and lowest risk. We use a rich dataset, which comprises key economic indicators and stock data for some specific companies, to measure how well these algorithms perform. Additionally, the research has also come up with new methods such as Stability, Execution Time, Total Function Evaluations (TFE) and Operations to give an inclusive picture of each algorithm's efficiency and usefulness. Outcomes demonstrate that Tabu Search (TS) showed excellent performance concerning returns, moderate risks as well as efficient execution time thereby making it the most stable option among all others;Differential Evolution (DE) and Particle Swarm Optimization (PSO) are appealing to risk-averse investors because they possess lower-risk profiles. The findings stress the importance of diversifying economic data to improve robustness of portfolio optimization model. In addition, future research should evaluate hybrid models as well as real time optimization frameworks so that computational efficiency and adaptability can be improved further © 2024 IEEE.

关键词： Computational Efficiency Differential Evolution Financial Portfolio Optimization metaheuristic algorithm Particle Swarm Optimization Returns Risk Stability Tabu Search

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A diversity metric for population-based metaheuristic algorithms

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INFORMATION SCIENCES 2022年 586卷 192-208页

作者： Osuna-Enciso, Valentin Cuevas, Erik Castaneda, Bernardo Morales Univ Guadalajara Blvd Marcelino Garcia Barragan 1421 Guadalajara 44430 Jalisco Mexico

In metaheuristic algorithms (MA), the balance between exploration and exploitation is a common issue considered an open research problem in the MA community. Besides its par-ticular parameters, another way to control the Exploration-Exploitation Balance in MA is by using a diversity metric (DM) as a guide. However, this procedure has two drawbacks: its computational cost and its effectiveness to represent the actual diversity of the popula-tion. This paper proposes a DM for real-coded candidate solutions. The approach utilizes surrogate hypervolumes to calculate the spatial distribution of the individuals in the pop-ulation. In a comparison against five DM reported in the literature, our proposal achieves comparable results in terms of stability, sensitivity, and robustness in the presence of out-liers, without significant increases in the computational cost. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Diversity metric metaheuristic algorithm Exploration-exploitation balance

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