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A simulation-metaheuristic approach for finding the optimal allocation of the battery energy storage system problem in distribution networks

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Decision Analytics Journal 2023年 7卷

作者： Sulaiman, Mohd Herwan Mustaffa, Zuriani Saari, Mohd Mawardi Jadin, Mohd Shawal Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Pekan Pahang 26600 Malaysia Faculty of Computing Universiti Malaysia Pahang Pekan Pahang 26600 Malaysia

This paper proposes a simulation study to solve the optimal allocation of the Battery Energy Storage System (BESS) problem in distribution networks. The effect of BESS's installation in the selected distribution networks is surveyed for a 24-hour period, where time-of-use electricity charges are divided into three periods: standard, peak, and off-peak hours. This study will use teaching learning-based optimization (TLBO) as the main optimizer for the problem simulation. The objective function is to minimize the combined cost of purchasing electricity and energy loss, where the optimal location of BESS and its operated power at each hour are treated as the control variables to be optimized. Two distribution systems are utilized, viz. 18-node and 33-node systems are considered to assess the performance of TLBO in solving the mentioned problem, where a comparison with other recent metaheuristic algorithms also have been conducted. The study's findings demonstrated the promising results of TLBO in terms of minimizing the energy cost and significantly reducing the peak loads during peak hours in the 24 h. The simulations also show that TLBO can be used as an effective tool for position and power of BESS optimization solution, where for the 18-node system, there is about 3.7 % cost reduction and for the 33-node system, about 12% cost saving for power purchased for the surveyed 24-h period. © 2023 The Author(s)

关键词： Battery energy storage system Distribution network Metaheuristic algorithm teaching learning-based optimization

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Advanced energy-based analyses of trusses employing hybrid metaheuristics

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2019年第9期28卷 e1609-e1609页

作者： Bekdas, Gebrail Kayabekir, Aylin Ece Nigdeli, Sinan Melih Toklu, Yusuf Cengiz Istanbul Univ Cerrahpasa Dept Civil Engn TR-34320 Istanbul Turkey Istanbul Beykent Univ Dept Civil Engn Istanbul Turkey

Total potential optimization using metaheuristic algorithm (TPO/MA) is an alternative method in structural analyses, and it is a black-box application for nonlinear analyses. In the study, an advanced TPO/MA using hybridization of several metaheuristic algorithms is investigated to solve large-scale structural analyses problems. The new generation algorithms considered in the study are flower pollination algorithm (FPA), teaching learning-based optimization, and Jaya algorithm (JA). Also, the proposed methods are compared with methodologies using classic and previously used algorithms such as differential evaluation, particle swarm optimization, and harmony search. Numerical investigations were carried out for structures with four to 150 degrees of freedoms (design variables). It has been seen that in several runs, JA gets trapped into local solutions. For that reason, four different hybrid algorithms using fundamentals of JA and phases of other algorithms, namely, JA using Levy flights, JA using Levy flights and linear distribution, JA with consequent student phase, and JA with probabilistic student phase (JA1SP), are developed. It is observed that among the variants tried, JA1SP is seen to be more effective on approaching to the global optimum without getting trapped in a local solution.

关键词： analyses of truss structures flower pollination algorithm hybrid algorithms Jaya algorithm teaching learning-based optimization total potential optimization using metaheuristic algorithm

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A new hybrid wrapper TLBO and SA with SVM approach for gene expression data

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INFORMATION SCIENCES 2019年 503卷 238-254页

作者： Shukla, Alok Kumar Singh, Pradeep Vardhan, Manu NIT Dept Comp Sci & Engn Chhattisgarh 492010 CG India

Gene expression dataset contains a small number of tissues and thousands or tens of thousands of noisy and redundant genes. This can lead to possibly overfitting and curse of dimensionality or even complete failure in the analysis of microarray. It can deteriorate the capability of the classification algorithms and also increase the computational burden. To overcome these challenges, in this study, we proposed a new hybrid wrapper approach to determine the optimal gene subsets from gene expression profiling. This proposed approach is a combination of teaching learning-based optimization (TLBO) and Simulated Annealing (SA) algorithm, called TLBOSA which can help to reveal the hidden patterns of tumors and enhance the interpretability of the selected genes. The proposed method comprises two steps. In the first step, Correlation-based Feature Selection (CFS) is applied to select subsets of optimal genes and filter the redundant genes from the biological datasets. In the second step, simulated annealing is incorporated with the TLBO algorithm, and used to increase the solution quality after each iteration of TLBO algorithm. It can also identify the subset of the most informative genes that can contribute to the accurate detection of cancer. A new encoding scheme is also introduced to transform the continuous version of TLBOSA to binary. It utilizes Support Vector Machine (SVM) classifier as a fitness function to select biomarkers that can classify biological tissues of binary and multi-class cancers. The performance of the proposed approach is evaluated on ten sets of microarray data and compared with well-known wrapper methods in the literature. Experimental results and statistical analysis demonstrate that the proposed method has significantly selected discriminating input genes and achieved high classification accuracy. Specifically, it achieves high prediction accuracy on Small-Blue-Round-Cell Tumour (SBRCT) data at 99.91% with 05 gene subsets. (C) 2019 Elsevier Inc

关键词： Accuracy Gene selection Support vector machine teaching learning-based optimization

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Gene selection for cancer types classification using novel hybrid metaheuristics approach

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SWARM AND EVOLUTIONARY COMPUTATION 2020年 54卷 100661-000页

作者： Shukla, Alok Kumar Singh, Pradeep Vardhan, Manu NIT Raipur Dept Comp Sci & Engn Chhattisgarh India

With the advancement of microarray technology, gene expression profiling has shown remarkable effort to predict the different types of malignancy and their subtypes. In microarrays, predicting highly discriminative genes is a challenging task and existing hybrid methods fail to deal with efficiently. To mitigate the curse of dimensionality problem and to improve the interpretability of discriminative genes, in this study, we developed a new hybrid wrapper approach which integrates the characteristics of teaching learning-based algorithm (TLBO) and gravitational search algorithm (GSA), called TLBOGSA. A new encoding strategy is also integrated into TLBOGSA to transmute the continuous search space to binary search space and form binary TLBOGSA. In the proposed method, firstly, minimum redundancy maximum relevance (mRMR) feature selection is employed to select relevant genes from the gene expression datasets. Then, wrapper method is applied to select the informative genes from the reduced data produced by mRMR. To improve the search capability during the evolution process, we have incorporated the gravitational search mechanism in the teaching phase. The proposed method uses naive bayes classifier as a fitness function to select the extremely judicious genes which can help to classify cancer accurately. The efficiency of proposed method is tested on ten biological datasets and compared with state-of-art computational intelligence approaches for tumor prediction. Experimental results and statistical analysis demonstrate that proposed method is significantly outperforms existing metaheuristic approaches regarding convergence rate, classification accuracy and optimal number of feature sets. The proposed method reaches above 98% classification accuracy in six datasets and maximum accuracy is achieved as 99.62% in DLBCL dataset.

关键词： Accuracy Classification Gene selection Naive bayes teaching learning-based optimization

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Hybrid Stochastic Ranking for Constrained optimization

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IEEE ACCESS 2020年 8卷 227270-227287页

作者： Jan, Muhammad Asif Sagheer, Muhammad Khan, Hidayat Ullah Uddin, Muhammad Irfan Khanum, Rashida Adeeb Mahmoud, Marwan Ikramullah Mast, Noor Kohat Univ Sci & Technol Inst Numer Sci Kohat 26000 Pakistan Abbottabad Univ Sci & Technol Dept Econ Abbottabad 22010 Pakistan Kohat Univ Sci & Technol Inst Comp Kohat 26000 Pakistan Univ Peshawar Jinnah Coll Women Peshawar 25000 Pakistan King Abdulaziz Univ Fac Appl Studies Jeddah 22785 Saudi Arabia Kohat Univ Sci & Technol Dept Phys Kohat 26000 Pakistan

teaching learning based optimization (TLBO) algorithm is a distinguished nature-inspired population-based meta-heuristic, which is basically designed for unconstrained optimization. TLBO mimics teaching learning process through which learners acquire knowledge from their teachers, and improve their results/grades, accordingly. Stochastic ranking (SR) is a constrained handling technique (CHT), which produces greediness among solutions to improve their fitness values and feasibility. Violation constraint handling (VCH) technique produces more feasibility among the existing superiority of feasibility CHTs due to its additional factor of ranking based on the number of constraints violated (NCV). This work brings in a new variant of SR, namely hybrid stochastic ranking (HSR), which combines SR and VCH. For constrained optimization, the integration of some CHT with TLBO is essential. In this paper, HSR is integrated with TLBO and a new constrained version of TLBO called HSR-TLBO is designed. The efficiency of HSR-TLBO is checked on constrained test functions of the suit CEC 2017. The experimental results show that HSR-TLBO got prominent position when compared and ranked with the top four papers and our two newly designed constrained variants of TLBO, MSR-TLBO and MVCH-TLBO, based on the provided budget and ranking criteria of the mentioned suit.

关键词： optimization Education Statistics Sociology Constraint handling Search problems Minimization teaching learning-based optimization meta-heuristics constrained handling techniques stochastic ranking superiority of feasibility violation constraint handling technique

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Optimal Combined Overcurrent and Distance Relay Coordination Using TLBO Algorithm 7th

Optimal Combined Overcurrent and Distance Relay Coordination...

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International Conference on Soft Computing for Problem Solving (SocProS)

作者： Roy, Saptarshi Babu, P. Suresh Babu, N. V. Phanendra Natl Inst Technol Warangal Warangal 506004 Telangana India

ISBN: (纸本)9789811315923;9789811315916

Relay coordination is an important aspect to maintain proper power system operation and control. Relays must be organized in such a way that every main relay should have a backup relay and coordination time interval (CTI) between primary and backup and different zones of the relays should be maintained to achieve proper fault identification and fault clearance sequences. The relays should operate in minimum desirable time satisfying all the coordination constraints. So, relay coordination is nothing but a highly constraint problem. Heuristic techniques are often used to get optimal solution of this kind of problem. In this paper, this constraint problem is solved by teaching learning-based optimization (TLBO) on a IEEE 14 bus system. Proper desirable time-setting multiplier (TSM) with minimum operating time of relays is calculated. We also incorporated intelligent overcurrent relay characteristics selection to get the desired results in this work. The results seem to be satisfactory as it is working better when compared with contemporary other techniques like genetic algorithm (GA) or particle swarm optimization (PSO).

关键词： Coordination of relays Coordination time interval teaching learning-based optimization Plug setting Time-setting multiplier Overcurrent relay characteristics

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Two novel combined approaches based on TLBO and PSO for a partial interdiction/fortification problem using capacitated facilities and budget constraint

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SOFT COMPUTING 2018年第17期22卷 5901-5919页

作者： Khanduzi, Raheleh Maleki, H. Reza Akbari, Reza Gonbad Kavous Univ Dept Math & Stat POB 49717-99151 Gonbad Kavous Golestan Iran Shiraz Univ Technol Fac Math POB 71555-313 Shiraz Iran Shiraz Univ Technol Dept Comp Engn & Informat Technol POB 71555-313 Shiraz Iran

In this paper, we introduce a novel continuous bi-level programming model of an interdiction/fortification problem, referred to as partial interdiction/fortification problem with capacitated facilities and Budget constraint (PIFCB). PIFCB is modeled as a Stackelberg game between a defender (leader) and an attacker (follower). The decisions of the defender are related to find optimal allocations of defensive resources as well as customer-facility assignments. So, the total system losses and demand-weighted distance are minimized. Following this action, the attacker seeks facilities to interdict for the capacity or service reduction and maximal losses resulting from a limited offensive budget. After modeling this problem, two combined methods, entitled particle swarm optimization with CPLEX (PSO-CPLEX) and teaching learning-based optimization with CPLEX (TLBO-CPLEX), are devised as solution procedures. For this bi-level programming problem, we executed two approaches which search the solution space of the defender's subproblem, according to PSO and TLBO principles, and corresponding attacker's subproblem is solved using CPLEX. To investigate the suggested methods, a comprehensive computational study is conducted and the effectiveness of the methods is compared together. The experimental results show the superiority of TLBO-CPLEX against PSO-CPLEX.

关键词： Continuous bi-level model Stackelberg game Capacitated facility Partial offense and defense Particle swarm optimization teaching learning-based optimization

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A hybrid fuzzy inference prediction strategy for dynamic multi-objective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2018年 43卷 147-165页

作者： Chen, Debao Zou, Feng Lu, Renquan Wang, Xude Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

Many real-world multi-objective optimization problems (MOPs) are dynamic in which variables of search space and/or objective space change over time. Hence the optimization algorithms should can quickly and efficiently track the Pareto front in dealing with dynamic environments. In this paper, a hybrid population prediction strategy based on fuzzy inference and one-step prediction (FIOPPS) is presented to extrapolate ahead the trajectory (position and/or orientation) of the new Pareto optimal solution set from the previous Pareto optimal solution sets and ensure the algorithm to respond quickly and effectively when the environment changes thus tracking the changing Pareto front. In our algorithm, the fuzzy inference model based on the Maximum Entropy Principle is extracted automatically from the previously found Pareto optimal solution sets to predict the Pareto solution sets at the beginning of the next time. Moreover, a new one-step prediction model is proposed to improve the prediction accuracy for environmental changes from motion state to static state and vice versa. Furthermore, a new variant of teaching-learning-based optimization algorithm with decomposition is first proposed as the MOEA optimizer for solving dynamic multi-objective optimization problems (DMOPs). In the proposed MOTLBO/D variant, the multi-objective decomposition mechanism is adopted and neighbor strategy is introduced into teaching-learning-based optimization algorithm (TLBO) to maintain the diversity of population and avoid the algorithm trapping into the local areas. Finally, to verify the performance of the proposed methods, ten benchmark test functions are simulated and evaluated. The statistical results indicate that the proposed FIOPPS strategy is promising for dealing with DMOPs.

关键词： Dynamic multi-objective optimization Decomposition teaching learning-based optimization Prediction Fuzzy inference

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Heuristic optimization based choice of LQR weighting matrices for a rotary inverted pendulum

Heuristic optimization based choice of LQR weighting matrice...

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IEEE International Conference on Recent Trends in Electrical, Control and Communication (RTECC)

作者： Nath, Krishanu Dewan, Lillie NIT Kurukshetra Dept Elect Engn Kurukshetra Haryana India

ISBN: (纸本)9781538643105

The choice of weighting matrices of the linear quadratic regulator (LQR) is one such decision which has a great effect on the dynamics of the system. There exists a nonlinear relationship between the dynamics of plant and their numerical values. Problems where such nonlinear relation exists, heuristic have served very useful to extract a better solution. In this paper, an attempt is made for the optimal choice of LQR weighting matrices using heuristic optimization techniques for a rotary inverted pendulum such that the settling time of the system is minimized.

关键词： Linear quadratic regulator teaching learning-based optimization Seeker optimization algorithm Gravitational search algorithm

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Modified meta-heuristics using random mutation for truss topology optimization with static and dynamic constraints

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2017年第2期4卷 106-130页

作者： Savsani, Vimal J. Tejani, Ghanshyam G. Patel, Vivek K. Savsani, Poonam Pandit Deendayal Petr Univ Dept Mech Engn Gandhinagar Gujarat India RK Univ Rajkot Sch Engn Dept Engn Mech Rajkot Gujarat India

In this paper, simultaneous size and topology optimization of planar and space trusses subjected to static and dynamic constraints are investigated. All the benchmark trusses consider discrete cross-sectional areas to consider the practical aspect of manufacturing. Moreover, Trusses are considered with multiple loading conditions and subjected to constraints for natural frequencies, element stresses, nodal displacements, Euler buckling criteria, and kinematic stability conditions. Truss topology optimization (TTO) can be accomplished by the removal of superfluous elements and nodes from the highly hyper static truss also known as the ground structure and results in the saving of the mass of the truss. In this method, the difficulties arise due to the singular solution and unnecessary analysis;therefore, FEA model is reformed to resolve these difficulties. The static and dynamic responses to the TTO problems are challenging due to its search space, which is implicit, non-convex, non-linear, and often leading to divergence. Modified meta-heuristics are effective optimization methods to handle such problems in actual fact. In this paper, modified versions of teaching learning-based optimization (TLBO), Heat Transfer Search (HTS), Water Wave optimization (WWO), and Passing Vehicle Search (PVS) are proposed by integrating the random mutation-based search technique with them. This paper compares the performance of four modified and four basic meta-heuristics to solve discrete TTO problems. (C) 2016 Society for Computational Design and Engineering. Publishing Servies by Elsevier.

关键词： teaching learning-based optimization Water Wave optimization Passing Vehicle Search Structural optimization Discrete optimization Heat Transfer Search

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