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backtracking search optimization algorithm with dual scatter search strategy for automated test case generation

JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2023年第7期35卷

作者： Ren, Jingru Zhu, Wenming Shenzhen Inst Informat Technol Dept Interdisciplinary Shenzhen Guangdong Peoples R China

It is a challenge to design an effective algorithm utilizing problem features in automated test case generation for path coverage (ATCG-PC). A feature of ATCG-PC "similar paths are usually executed by similar test cases" was touched by a few scholars and can be further exploited to design more effective algorithms. Inspired by this feature, this paper proposes a two-stage local search strategy, denoted dual scatter search (DS) strategy, which concatenates two improved scatter search strategies with different search behaviors. The first stage aims to fully exploit the discovered test cases to search for desired test cases, and the latter stage aims to mine the unexploited areas of the first stage via using less computational overhead. Then, a backtracking search optimization algorithm with dual scatter search strategy (BSADS) is proposed, which incorporates DS strategy into the backtracking search optimization algorithm (BSA) with strong exploration capability. BSA is first introduced into the field of ATCG-PC. The performance of BSA-DS and some state-of-the-art algorithms is tested on twelve popular benchmark programs. Experimental studies demonstrate that BSA-DS achieves the highest path coverage with the fewest test cases and running time on at least eight out of the twelve programs.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Automated test case generation backtracking search optimization algorithm Dual scatter search strategy Path coverage

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An improved particle swarm optimization with backtracking search optimization algorithm for solving continuous optimization problems

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 4期38卷 2797-2831页

作者： Zaman, Hamid Reza Rafat Gharehchopogh, Farhad Soleimanian Islamic Azad Univ Urmia Branch Dept Comp Engn Orumiyeh Iran

The particle swarm optimization (PSO) is a population-based stochastic optimization technique by the social behavior of bird flocking and fish schooling. The PSO has a high convergence rate. It is prone to losing diversity along the iterative optimization process and may get trapped into a poor local optimum. Overcoming these defects is still a significant problem in PSO applications. In contrast, the backtracking search optimization algorithm (BSA) has a robust global exploration ability, whereas, it has a low local exploitation ability and converges slowly. This paper proposed an improved PSO with BSA called PSOBSA to resolve the original PSO algorithm's problems that BSA's mutation and crossover operators were modified through the neighborhood to increase the convergence rate. In addition to that, a new mutation operator was introduced to improve the convergence accuracy and evade the local optimum. Several benchmark problems are used to test the performance and efficiency of the proposed PSOBSA. The experimental results show that PSOBSA outperforms other well-known metaheuristic algorithms and several state-of-the-art PSO variants in terms of global exploration ability and accuracy, and rate of convergence on almost all of the benchmark problems.

关键词： Particle swarm optimization backtracking search optimization algorithm Solving continuous optimization problems Hybridization

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Optimal Sizing of Islanded Microgrid using the backtracking search optimization algorithm with Standard Brownian Walk 10

Optimal Sizing of Islanded Microgrid using the Backtracking ...

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10th IEEE International Conference on Power Electronics, Drives and Energy Systems (IEEE PEDES)

作者： Abongmbo, Simon Benhaddou, Driss Krishnamoorthy, Harish Fan, Lei Shi, Jian Univ Houston Elect & Comp Engn Dept Houston TX 77004 USA Alfaisal Univ Coll Engn Riyadh Saudi Arabia Univ Houston Engn Technol Houston TX USA

ISBN: (纸本)9781665455664

In this paper, the backtracking search optimization (BSA) algorithm is developed for the optimal sizing of an island microgrid consisting of a wind energy system, photovoltaic (PV) system, diesel generator, and battery energy storage system (BESS). The objective is to determine the optimal sizes and combinations of the various available power production and storage component technologies such that the total annual investment and operation cost is minimized and all system-related resiliency and reliability limitations are satisfied. To optimize the utilization of renewable sources, a renewable fraction restriction is applied, and the total problem is formulated as a constrained optimization problem with the sizes of the power generation and storage components serving as the decision variables. The suggested rules-based Energy Management algorithm (EMA) controls the coordination of the various energy sources. The implementation is performed in MATLAB, and the results are utilized to evaluate the proposed methodology. The results show that converges faster than the classic Genetic algorithm (GA) by about 51%, and is more stable to perturbations of initial control parameters.

关键词： Annual Investment and Operation Cost backtracking search optimization algorithm Energy Management algorithm Islanded Microgrid Optimal sizing system reliability

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backtracking search optimization algorithm-based least square support vector machine and its applications

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 94卷 103801-103801页

作者： Tian, Zhongda Shenyang Univ Technol Sch Artificial Intelligence Shenyang 110870 Peoples R China

Based on statistical learning theory, least square support vector machine can effectively solve the learning problem of small samples. However, the parameters of the least square support vector machine model have a great influence on its performance. At the same time, there is no clear theoretical basis for how to choose these parameters. In order to cope with the parameters optimization of the least square support vector machine, a backtracking search optimization algorithm-based least square support vector machine model is proposed. In this model, backtracking search optimization algorithm is introduced to optimize the parameters of the least square support vector machine. Meanwhile, the least square support vector machine model is updated by the prediction error combined with the sliding window strategy to solve the problem of mis-match between the prediction model and the actual sample data in the time-varying system. The performance of the proposed model is verified by classification and regression problems. The classification performance of the model is verified by five Benchmark datasets, and the regression prediction performance is verified by the dynamic liquid level of the oil production process. Compared with genetic algorithm, particle swarm optimization algorithm, and improved free search algorithm optimized least square support vector machine, the simulation results show that the proposed model has higher classification accuracy with less computation time, and higher prediction accuracy and reliability for the dynamic liquid level. The proposed model is effective.

关键词： backtracking search optimization algorithm Least square support vector machine Dynamic fluid level Benchmark dataset Classification Prediction

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Adaptive backtracking search optimization algorithm with a dual-learning strategy for dynamic economic dispatch with valve-point effects

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ENERGY 2022年第0期248卷 123558-123558页

作者： Hu, Zhongbo Dai, Canyun Su, Qinghua Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China

Dynamic economic dispatch with valve-point effect (DED_vpe) is a dynamic nonlinear high-dimensional optimization problem with non-smooth and non-convex characteristics. Meta-heuristic methods have become the mainstream for solving the DED_vpe problem. However, most of these methods only focus on minimizing the generation costs and ignore the algorithmic robustness. In this paper, an adaptive backtracking search optimization algorithm with a dual-learning strategy (DABSA) is proposed for solving the DED_vpe problem. In DABSA, a dual-learning strategy (DL) based on the current and historical optimal individuals is developed to update each individual. This updating strategy helps DABSA improve solution accuracy and overcome premature convergence. In addition, an adaptive parameter control mechanism (APC), which can automatically adjust parameter 'mixrate' value according to the current iteration number, is presented. To handle the system constraints, a 'repair thorn penalty' constraints handling approach is employed to lead non-feasible solutions towards the feasible region quickly. The performance of DABSA is assessed by testing on four DED problems containing 5, 10 and 30 units. The experimental results show that DABSA is very competitive compared with reported representative methods in yielding low fuel costs along with high robustness.

关键词： backtracking search optimization algorithm Dynamic economic dispatch Valve-point effects

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An adaptive hybrid backtracking search optimization algorithm for dynamic economic dispatch with valve-point effects

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ENERGY 2022年第PartE期239卷 122461-122461页

作者： Dai, Canyun Hu, Zhongbo Su, Qinghua Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China

Dynamic economic dispatch with valve-point effects (DED_vpe) is a high-dimensional constrained optimization problem with non-convex and non-smooth characteristics. Hybrid methods are one of the most advanced methods to solve the problem. However, most of these methods improve the solution accuracy at the expense of algorithm robustness. This paper proposes an adaptive hybrid backtracking search optimization algorithm (AHBSA) for solving the DED_vpe. The core idea of AHBSA lies in designing a suitable coupling structure based on the current best individual (called optimal partial coupling). The structure hybridizes an improved BSA mutation operator and the DE/best/1 operator with equal probability. The improved BSA mutation operator uses the current best individual and the historical population to update individual position, called BSA/best/old. It is also the first research work of extending BSA to the problem. In addition, an adaptive parameter control mechanism is proposed to select an appropriate 'mixrate' value for achieving better coupling. The performance of AHBSA is validated on six DED test cases of three systems. Experimental results demonstrate that, compared with some representative methods, AHBSA not only reduces the fuel cost but also ensures the robustness of the algorithm. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： backtracking search optimization algorithm Dynamic economic dispatch Robustness Solution accuracy Valve-point effects

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Advanced backtracking search optimization algorithm for a new joint replenishment problem under trade credit with grouping constraint

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APPLIED SOFT COMPUTING 2020年 86卷

作者： Wang, Lin Peng, Lu Wang, Sirui Liu, Shan Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China Xi An Jiao Tong Univ Sch Management Xian 710049 Shaanxi Peoples R China

In the real business situation, suppliers usually provide retailers with forward financing to decrease inventory or increase demand. Moreover, some heterogeneous goods are not allowed to transport together, or a penalty cost is incurred when heterogeneous goods are transported at the same time. This research proposes a practical multi-item joint replenishment problem (JRP) by considering trade credit and grouping constraint in accordance with the practical situation. The JRP aims to find reasonable item replenishment frequencies and each group's basic replenishment cycle time so that the overall cost can be minimized. Four intelligent algorithms, which include an advanced backtracking search optimization algorithm (ABSA), genetic algorithm (GA), differential evolution (DE) and backtracking search optimization algorithm (BSA), are provided to solve this problem. Findings of contrastive example verify that ABSA is superior to GA, DE, and BSA, which have been validated to be effective algorithms. Randomly generated problems are used to test the performance of ABSA. Results indicate ABSA is more effective and stable to resolve the proposed JRP than the other algorithms. ABSA is a good solution for the proposed JRP with heterogeneous items under trade credits. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Joint replenishment problem Trade credit Grouping constraint backtracking search optimization algorithm

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Optimizing echo state network with backtracking search optimization algorithm for time series forecasting

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 81卷 117-132页

作者： Wang, Zhigang Zeng, Yu-Rong Wang, Sirui Wang, Lin Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China YGsoft Inc Yuanguang Software Pk Technol & Innovat Coast Zhuhai 519085 Peoples R China Hubei Univ Econ Wuhan 430205 Hubei Peoples R China

The echo state network (ESN) is a state-of-the art reservoir computing approach, which is particularly effective for time series forecasting problems because it is coupled with a time parameter. However, the linear regression algorithm commonly used to compute the output weights of ESN could usually cause the trained network over-fitted and thus obtain unsatisfactory results. To overcome the problem, we present four optimized ESNs that are based on the backtracking search optimization algorithm (BSA) or its variants to improve generalizability. Concretely, we utilize BSA and its variants to determine the most appropriate output weights of ESN given that the optimization problem is complex while BSA is a novel evolutionary algorithm that effectively unscrambles optimal solutions in complex spaces. The three BSA variants, namely, adaptive population selection scheme (APSS)-BSA, adaptive mutation factor strategy (AMFS)-BSA, and APSS&AMFS-BSA, were designed to further improve the performance of BSA. Time series forecasting experiments were performed using two real-life time series. The experimental results of the optimized ESNs were compared with those of the basic ESN without optimization, and the two other comparison approaches, as well as the other existing approaches. Experimental results showed that (a) the results of the optimized ESNs are more accurate than that of basic ESN and (b) APSS&AMFS-BSA-ESN nearly outperforms basic ESN, the three other optimized ESNs, the two comparison approaches, and other existing optimization approaches.

关键词： Echo state network Evolutionary algorithm backtracking search optimization algorithm Time series forecasting

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Optimizing SVM's parameters based on backtracking search optimization algorithm for gear fault diagnosis

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JOURNAL OF VIBROENGINEERING 2019年第1期21卷 66-81页

作者： Vantrong Thai Cheng, Junsheng Viethung Nguyen Phuonganh Daothi Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha Hunan Peoples R China Hanoi Univ Ind Fac Mech Engn Hanoi Vietnam Univ Econ & Tech Ind Fac Informat Technol Hanoi Vietnam

The accuracy of a support vector machine (SVM) classifier is decided by the selection of optimal parameters for the SVM. The backtracking search optimization algorithm (BSA) is often applied to resolve the global optimization problem and adapted to optimize SVM parameters. In this research, a SVM parameter optimization method based on BSA (BSA-SVM) is proposed, and the BSA-SVM is applied to diagnose gear faults. Firstly, a gear vibration signal can be decomposed into several intrinsic scale components (ISCs) by means of the Local Characteristics-Scale Decomposition (LCD). Secondly, the MPE can extract the fault feature vectors from the first few ISCs. Thirdly, the fault feature vectors are taken as the input vectors of the BSA-SVM classifier. The analysis results of BSA-SVM classifier show that this method has higher accuracy than GA (Genetic algorithm) or PSO (Particles Swarm algorithm) algorithms combined with SVM. In short, the BSA-SVM based on the MPE-LCD is suitable to diagnose the state of health gear.

关键词： signal processing fault detection gears artificial neural networks backtracking search optimization algorithm

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Quaternion-Based backtracking search optimization algorithm

Quaternion-Based Backtracking Search Optimization Algorithm

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Passos, Leandro Aparecido Rodrigues, Douglas Papa, Joao Paulo UFSCar Fed Univ Sao Carlos Dept Comp Sao Carlos SP Brazil UNESP Sao Paulo State Univ Sch Sci Bauru SP Brazil

ISBN: (纸本)9781728121536

Fitness landscape has been one of the main limitations regarding optimization tasks. Although meta-heuristic techniques have achieved outstanding results over a large variety of problems, some issues related to the function geometry and the risk to get trapped from local optima are issues that still require attention. To deal with this problem, we propose the Quaternion-based backtracking search optimization algorithm, a variant of the standard backtracking search optimization algorithm that maps each decision variable in a tensor onto a hypercomplex search space, whose landscape is expected to be smoother. Experiments conducted using nine benchmarking functions showed considerably better results than the ones achieved over standard search spaces, as well as more accurate results than some quaternion-based methods as well.

关键词： backtracking search optimization algorithm Quaternions Meta-heuristics

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