A Binary Sine-Cosine Algorithm Applied to the Knapsack Problem

One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.

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An iterative dynamic programming approach for the temporal knapsack problem

European Journal of Operational Research ◽

10.1016/j.ejor.2020.12.036 ◽

2020 ◽

Author(s):

F. Clautiaux ◽

B. Detienne ◽

G. Guillot

Keyword(s):

Dynamic Programming ◽

Knapsack Problem ◽

Programming Approach ◽

Dynamic Programming Approach ◽

Iterative Dynamic Programming

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An enhanced binary slime mould algorithm for solving the 0–1 knapsack problem

Engineering With Computers ◽

10.1007/s00366-021-01470-z ◽

2021 ◽

Author(s):

Benyamin Abdollahzadeh ◽

Saeid Barshandeh ◽

Hatef Javadi ◽

Nicola Epicoco

Keyword(s):

Knapsack Problem ◽

Slime Mould

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Optimization of sliding mode control with PID surface for robot manipulator by Evolutionary Algorithms

Open Computer Science ◽

10.1515/comp-2020-0144 ◽

2020 ◽

Vol 10 (1) ◽

pp. 396-407

Author(s):

Fatiha Loucif ◽

Sihem Kechida

Keyword(s):

Sliding Mode ◽

Robot Manipulator ◽

Absolute Error ◽

Grey Wolf Optimizer ◽

Optimal Parameters ◽

Error Criterion ◽

Trajectory Tracking Control ◽

Integral Time ◽

Sine Cosine Algorithm ◽

Antlion Optimization

AbstractIn this paper, a sliding mode controller (SMC) with PID surface is designed for the trajectory tracking control of a robot manipulator using different optimization algorithms such as, Antlion Optimization Algorithm (ALO) Sine Cosine Algorithm (SCA) Grey Wolf Optimizer (GWO) and Whale Optimizer Algorithm (WOA). The aim of this work is to introduce a novel SMC-PID-ALO to control nonlinear systems, especially the position of two of the joints of a 2DOF robot manipulator. The basic idea is to determinate four optimal parameters (Kp, Ki, Kd and lamda) ensuring the best performance of a robot manipulator system, minimizing the integral time absolute error criterion (ITAE) and the integral time square error criterion (ISTE). The robot manipulator is modeled in Simulink and the control is implemented using the MATLAB environment. The obtained simulation results prove the robustness of ALO in comparison with other algorithms.

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State of charge estimation for lithium-ion batteries using dynamic neural network based on sine cosine algorithm

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211018038 ◽

2021 ◽

pp. 095440702110180

Author(s):

Meng Wei ◽

Min Ye ◽

Jia Bo Li ◽

Qiao Wang ◽

Xin Xin Xu

Keyword(s):

Neural Network ◽

Lithium Ion Batteries ◽

Unscented Kalman Filter ◽

Lithium Ion ◽

State Of Charge ◽

Support Vector ◽

Battery Management System ◽

Dynamic Neural Network ◽

Soc Estimation ◽

Sine Cosine Algorithm

State of charge (SOC) of the lithium-ion batteries is one of the key parameters of the battery management system, which the performance of SOC estimation guarantees energy management efficiency and endurance mileage of electric vehicles. However, accurate SOC estimation is a difficult problem owing to complex chemical reactions and nonlinear battery characteristics. In this paper, the method of the dynamic neural network is used to estimate the SOC of the lithium-ion batteries, which is improved based on the classic close-loop nonlinear auto-regressive models with exogenous input neural network (NARXNN) model, and the open-loop NARXNN model considering expected output is proposed. Since the input delay, feedback delay, and hidden layer of the dynamic neural network are usually selected by empirically, which affects the estimation performance of the dynamic neural network. To cover this weakness, sine cosine algorithm (SCA) is used for global optimal dynamic neural network parameters. Then, the experimental results are verified to obtain the effectiveness and robustness of the proposed method under different conditions. Finally, the dynamic neural network based on SCA is compared with unscented Kalman filter (UKF), back propagation neural network based on particle swarm optimization (BPNN-PSO), least-squares support vector machine (LS-SVM), and Gaussian process regression (GPR), the results show that the proposed dynamic neural network based on SCA is superior to other methods.

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A meliorated Harris Hawks optimizer for combinatorial unit commitment problem with photovoltaic applications

Journal of Electrical Systems and Information Technology ◽

10.1186/s43067-020-00026-3 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Ayani Nandi ◽

Vikram Kumar Kamboj

Keyword(s):

Power Generation ◽

Power System ◽

Electric Power ◽

Unit Commitment ◽

Renewable Energy Sources ◽

Electric Power System ◽

Unit Commitment Problem ◽

Spinning Reserve ◽

Commitment Problem ◽

Sine Cosine Algorithm

AbstractConventional unit commitment problem (UCP) consists of thermal generating units and its participation schedule, which is a stimulating and significant responsibility of assigning produced electricity among the committed generating units matter to frequent limitations over a scheduled period view to achieve the least price of power generation. However, modern power system consists of various integrated power generating units including nuclear, thermal, hydro, solar and wind. The scheduling of these generating units in optimal condition is a tedious task and involves lot of uncertainty constraints due to time carrying weather conditions. This difficulties come to be too difficult by growing the scope of electrical power sector day by day, so that UCP has connection with problem in the field of optimization, it has both continuous and binary variables which is the furthermost exciting problem that needs to be solved. In the proposed research, a newly created optimizer, i.e., Harris Hawks optimizer (HHO), has been hybridized with sine–cosine algorithm (SCA) using memetic algorithm approach and named as meliorated Harris Hawks optimizer and it is applied to solve the photovoltaic constrained UCP of electric power system. In this research paper, sine–cosine Algorithm is used for provision of power generation (generating units which contribute in electric power generation for upload) and economic load dispatch (ELD) is completed by Harris Hawks optimizer. The feasibility and efficacy of operation of the hybrid algorithm are verified for small, medium power systems and large system considering renewable energy sources in summer and winter, and the percentage of cost saving for power generation is found. The results for 4 generating units, 5 generating units, 6 generating units, 7 generating units, 10 generating units, 19 generating units, 20 generating units, 40 generating units and 60 generating units are evaluated. The 10 generating units are evaluated with 5% and 10% spinning reserve. The efficacy of the offered optimizer has been verified for several standard benchmark problem including unit commitment problem, and it has been observed that the suggested optimizer is too effective to solve continuous, discrete and nonlinear optimization problems.

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