An improved teaching-learning-based optimization for constrained evolutionary optimization

Wang, Bing-Chuan; Li, Han-Xiong; Feng, Yongjin

doi:10.1016/j.ins.2018.04.083

Cited by 36 publications

(12 citation statements)

References 38 publications

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“…In summary, the experimental results validate that DeCODE yields better or similar performance compared with other four competitors on the 24 test functions from IEEE CEC2006. were selected as the competitors: ITLBO [59], FROFI [54], CACDE [60], AIS-IRP [61], and DW [48]. The experimental results of ITLBO and FROFI can be available from our previous study.…”

Section: B Experiments On the 24 Benchmark Test Functions From Ieee mentioning

confidence: 99%

Decomposition-Based Multiobjective Optimization for Constrained Evolutionary Optimization

Wang

Zhang

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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Pareto dominance-based multiobjective optimization has been successfully applied to constrained evolutionary optimization during the last two decades. However, as another famous multiobjective optimization framework, decompositionbased multiobjective optimization has not received sufficient attention from constrained evolutionary optimization. In this paper, we make use of decomposition-based multiobjective optimization to solve constrained optimization problems (COPs). In our method, first of all, a COP is transformed into a biobjective optimization problem (BOP). Afterward, the transformed BOP is decomposed into a number of scalar optimization subproblems. After generating an offspring for each subproblem by differential evolution, the weighted sum method is utilized for selection. In addition, to make decompositionbased multiobjective optimization suit the characteristics of constrained evolutionary optimization, weight vectors are elaborately adjusted. Moreover, for some extremely complicated COPs, a restart strategy is introduced to help the population jump out of a local optimum in the infeasible region. Extensive experiments on three sets of benchmark test functions, namely, 24 test functions from IEEE CEC2006, 36 test functions from IEEE CEC2010, and 56 test functions from IEEE CEC2017, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods.

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Section: B Experiments On the 24 Benchmark Test Functions From Ieee mentioning

confidence: 99%

Decomposition-Based Multiobjective Optimization for Constrained Evolutionary Optimization

Wang

Zhang

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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“…Table 7 gives the mean and standard deviation of objective values with respect to CETDE and nine competing algorithms. Among all nine competitors, the first four are DEbased methods (i.e., DPDE [31], rank-iMDDE [36], eDE [44], and FRC-CEA [28]), the next four are non-DE-based methods (i.e., AIS [45], CMPSOWV [46], I-ABC [47] and ITLBO [48]), and the last one is a PSO-and DE-based hybrid method (i.e., DPD [49]). Since the best feasible optimal values are known, we compare the mean function values with the best known optima.…”

Section: B General Performance Of Cetde On Ieee Cec2006 Problemsmentioning

confidence: 99%

“…[31] 5126.4967 ± 0.00E+00 * -6961.8139 ± 0.00E+00 * 24.3062 ± 6.25E-09 * -0.0958 ± 0.00E+00 * rank-iMDDE [36] 5126. [31] 680.6301 ± 3.65E-14 * 7049.2480 ± 8.36E-08 * 0.7499 ± 0.00E+00 * -1.0000 ± 0.00E+00 * rank-iMDDE [36] 680.6300 ± 1.56E-06 * 7049.2490 ± 8.24E-04 0.7499 ± 1.61E-07 * -1.0000 ± 0.00E+00 * eDE [44] 680.6301 ± 1.10E-25 * 7049.2480 ± 4.89E-19 * 0.7499 ± 1.28E-32 * -1.0000 ± 0.00E+00 * FRC-CEA [28] 680.6300 ± 2.49E-13 * 7049.2480 ± 1.40E-04 * 0.7499 ± 0.00E+00 * -1.0000 ± 0.00E+00 * AIS [45] 680.6300 ± 0.00E+00 * 7049.5700 ± 4.50E-04 0.7499 ± 1.40E-08 * -1.0000 ± 0.00E+00 * CMPSOWV [46] 680.6300 ± 2.76E-12 * 7085.1718 ± 7.13E+02 0.7499 ± 1.03E-12 * -1.0000 ± 0.00E+00 * I-ABC [47] 680.6330 ± 1.53E-03 7124.0420 ± 5.94E+01 0.7499 ± 2.44E-06 * -1.0000 ± 0.00E+00 * ITLBO [48] 680.6300 ± 3.36E-13 * 7049.2490 ± 4.29E-05 0.7499 ± 1.13E-16 * -1.0000 ± 0.00E+00 * DPD [49] 680.6301 ± 0.00E+00 * 7049.2480 ± 0.00E+00 * 0.7499 ± 0.00E+00 * -1.0000 ± 0.00E+00 * CETDE 680.6301 ± 1.88E-09 * 7049.2480 ± 6.24E-08 * 0.7499 ± 1.54E-09 * -1.0000 ± 0.00E+00 * G13 G14 G15 G16 DPDE [31] 0.0539 ± 1.16E-17 * -47.7649 ± 2.56E-09 * 961.7150 ± 0.00E+00 * -1.9052 ± 0.00E+00 * rank-iMDDE [36] 0. [28] -398.2812 ± 6.27E+00 -5.5080 ± 0.00E+00 * 19 AIS [45] -399.8743 ± 2.00E+00 -5.5080 ± 0.00E+00 * 15 CMPSOWV [46] -360.2836 ± 1.43E+01 -5.5080 ± 0.00E+00 * 16 I-ABC [47] 169.0210 ± 3.67E+02 -5.5080 ± 1.78E-15 * 7 ITLBO [48] -256.4000 ± 1.42E+02 -5.5080 ± 9.06E-16 * 15 DPD [49] -398.1809 ± 2.97E+00 -5.5080 ± 0.00E+00 * 20 CETDE -399.4736 ± 2.88E+00 -5.5080 ± 5.69E-11 * 19 This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: G01mentioning

confidence: 99%

Constrained Optimization Based on Ensemble Differential Evolution and Two-Level-Based Epsilon Method

Zhang

2020

IEEE Access

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Constrained optimization problems (COPs) are common in many fields, and the search algorithm and constraint handling technique play important roles in the constrained evolution algorithms. In this paper, we propose a new optimization algorithm named CETDE based on ensemble differential evolution (DE) and a two-level epsilon-constrained method. In the ensemble DE variant, some promising parameters and mutation strategies constitute the candidate pool, and each element in the pool coexists throughout the search process and competes to generate new solutions. The two-level epsilon method is proposed by incorporating a generation and a population comparison level to retain more promising solutions without degrading the solution quality. Moreover, a diversity promotion scheme is developed to improve the population distribution when the search becomes trapped in a small region. The superior performance of CETDE is validated by comparison with some state-of-the-art COEAs over two sets of artificial benchmarks and five real-world problems. The competitive results show that CETDE is an effective method for solving COPs.

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“…Because TLBO has the advantages of few parameters, simple thinking, easy understanding and strong robustness [1][2][3][4], it has attracted the attention of many scholars since it was put forward and has been applied in many fields. Such as reactive power optimization of power system [5], LQR controller optimization [6], IIR digital filter design [7], steelmaking and continuous casting scheduling problem [8], PID controller parameter optimization problem [9,10], feature selection problem [11], HVDC optimization of voltage source converter [12], extension of global optimization technology to constrained optimization [13], analysis of financial time series data [14], neural network optimization [15], etc. Compared with the existing swarm intelligence algorithm, the algorithm obtains better results.…”

Section: Introductionmentioning

confidence: 99%

Research on Geographic Location Prediction Algorithm Based on Improved Teaching and Learning Optimization ELM

Yang

Sun

2020

Front. Phys.

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With the rapid development of vehicle-mounted communication technology, GPS data is an effective method to predict the current road vehicle track based on vehicle-mounted data. GPS-oriented vehicle-mounted data position prediction method is currently a hot research work and an effective method to realize intelligent transportation. In this paper, an improvement scheme is proposed based on the problem of falling into local optimization existing in the basic algorithm of teaching and learning optimization algorithm. An interference operator is used to disturb teachers to enhance the kinetic energy of the population to jump out of local optimization. By comparing the performance of GA, PSO, TLBO, and ITLBO algorithms with four test functions, the results show that ITLBO has efficient optimization effect and generalization ability. Finally, the ITLBO-ELM algorithm has the best prediction effect by comparing the vehicle GPS data and comparing the experimental algorithms.

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An improved teaching-learning-based optimization for constrained evolutionary optimization

Cited by 36 publications

References 38 publications

Decomposition-Based Multiobjective Optimization for Constrained Evolutionary Optimization

Decomposition-Based Multiobjective Optimization for Constrained Evolutionary Optimization

Constrained Optimization Based on Ensemble Differential Evolution and Two-Level-Based Epsilon Method

Research on Geographic Location Prediction Algorithm Based on Improved Teaching and Learning Optimization ELM

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