Simulated Annealing Particle Swarm Optimization Research Articles

A Precoding Approach for Dual-Functional Radar-Communication System With One-Bit DACs

In this paper, we investigate the precoder design for multiple-input multiple-output (MIMO) dual-functional radar-communication (DFRC) system with one-bit digital-to-analog converters (DACs). In order to form the dual-functional beam-pattern, we formulate the precoding problem as a weighted optimization problem with the constant modulus constraint, which aims at minimizing the average error power and guaranteeing radar waveform similarity. The problem is divided into three sub-problems corresponding to the multiple variables, i.e., the precoding factor, transmit signal matrix, and radar waveform matrix. Due to the discrete and non-convex properties of the optimization problem, we propose a multi-variable alternating minimization (MVAM) framework to achieve the near-optimal solutions. The precoding factor and radar waveform can be solved in closed-forms. For the transmit signal matrix, we devise a binary particle swarm optimization-simulated annealing (BPSO-SA) algorithm to obtain it under the MVAM framework. Extensive simulations validate the effectiveness of the proposed approach under various scenarios, including the case without perfect channel state information. The simulation results show that, compared with existing non-linear precoders, the proposed approach achieves 7dB SNR gain at the bit error rate of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> in the 8-antenna system, and the gain of SNR is 0.2dB in the massive MIMO system with 128 antennas.

Short-Term Power Load Forecasting Based on SAPSO-CNN-LSTM Model considering Autocorrelated Errors

Accurate power load forecasting is essential for power grid operation and dispatching. To further improve the accuracy of power load forecasting, this study proposes a new power load forecasting method. Firstly, correlation coefficients of influential variables are calculated for feature selection. Secondly, the form of input data is changed to adjust for autocorrelated errors. Thirdly, data features are extracted by convolutional neural networks (CNN) to construct feature vectors. Finally, the feature vectors are input into long short-term memory (LSTM) network for training to obtain prediction results. Moreover, for solving the problem that network hyperparameters are difficult to set, the simulated annealing particle swarm optimization (SAPSO) algorithm is used to optimize the hyperparameters. Experiments show that the prediction accuracy of the proposed model is higher compared with LSTM, CNN-LSTM, and other models.

Research on an Optimization Method for Injection-Production Parameters Based on an Improved Particle Swarm Optimization Algorithm

The optimization of injection–production parameters is an important step in the design of gas injection development schemes, but there are many influencing factors and they are difficult to determine. To solve this problem, this paper optimizes injection-production parameters by combining an improved particle swarm optimization algorithm to study the relationship between injection-production parameters and the net present value. In the process of injection-production parameter optimization, the particle swarm optimization algorithm has shortcomings, such as being prone to fall into local extreme points and slow in convergence speed. Curve adaptive and simulated annealing particle swarm optimization algorithms are proposed to further improve the optimization ability of the particle swarm optimization algorithm. Taking the Tarim oil field as an example, in different stages, the production time, injection volume and flowing bottom hole pressure were used as input variables, and the optimal net present value was taken as the goal. The injection-production parameters were optimized by improving the particle swarm optimization algorithm. Compared with the particle swarm algorithm, the net present value of the improved scheme was increased by about 3.3%.

Optimal Flood-Control Operation of Cascade Reservoirs Using an Improved Particle Swarm Optimization Algorithm

Optimal reservoir operation is an important measure for ensuring flood-control safety and reducing disaster losses. The standard particle swarm optimization (PSO) algorithm can find the optimal solution of the problem by updating its position and speed, but it is easy to fall into a local optimum. In order to prevent the problem of precocious convergence, a novel simulated annealing particle swarm optimization (SAPSO) algorithm was proposed in this study, in which the Boltzmann equation from the simulated annealing algorithm was incorporated into the iterative process of the PSO algorithm. Within the maximum flood peak reduction criterion, the SAPSO algorithm was used into two floods in the Tianzhuang–Bashan cascade reservoir system. The results shown that: (1) There are lower maximum outflows. The maximum outflows of Tianzhuang reservoir using SAPSO algorithm decreased by 9.3% and 8.6%, respectively, compared with the measured values, and those of Bashan reservoir decreased by 18.5% and 13.5%, respectively; (2) there are also lower maximum water levels. The maximum water levels of Tianzhuang reservoir were 0.39 m and 0.45 m lower than the measured values, respectively, and those of Bashan reservoir were 0.06 m and 0.46 m lower, respectively; and (3) from the convergence processes, the SAPSO algorithm reduced the convergence speed in the early stage of convergence and provided a superior objective function value than PSO algorithm. At the same time, by comparing with GA algorithm, the performance and applicability of SAPSO algorithm in flood operation are discussed further. Thus, the optimal operation model and SAPSO algorithm proposed in this study provide a new approach to realizing the optimal flood-control operation of cascade reservoir systems.

Toward Energy Footprint Reduction of a Machining Process

In a machining process, proper selection of process plans and cutting parameters can effectively reduce energy consumption and shorten production time. Traditionally, studies on process planning and cutting parameter optimization for energy saving are mostly concentrated on electrical energy consumption. Since the preparation process of cutting tools and cutting fluid consumes a considerable amount of energy, conservation of this part of energy consumption, namely, the embodied energy consumption, will achieve a more energy-efficient machining process. In this article, an integrated model for process planning and cutting parameter optimization is proposed to shorten production time and reduce the energy footprint (namely, electrical energy consumption and embodied energy consumption of cutting tools and cutting fluid) of a machining process. Considering that the optimization of process plan and cutting parameters in an integrated manner is a hybrid programming process, simulated annealing and quantum-behaved particle swarm optimization (SA-QPSO) hybrid algorithm is employed to solve the proposed model. Results of the case study show that: 1) embodied energy consumption of cutting tools and cutting fluid accounts for a nonnegligible proportion of energy footprint of the machining process and 2) there is a tradeoff between energy footprint and production time, and the balance of them is achieved through the proposed optimization approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article, for the first time, to the best of our knowledge, proposes an integrated approach to reduce both electrical and embodied energy consumption of a machining process through optimizing process plan and cutting parameters. Such broader consideration makes this integrated optimization approach more applicable to real industry settings and contributes to the comprehensive improvement of energy efficiency in the machining process. To better use this approach, the following three steps should be highlighted: 1) the energy footprint characteristics of the machining process should be comprehensively analyzed and modeled; 2) the integrated optimization model for minimizing energy footprint and production time needs to cooperate with machining constraints, such as process centralization, machining sequence, and process requirements; and 3) solving the proposed model is a hybrid programming process since there are discrete decision variables and continuous variables. A proper algorithm should be used to solve the proposed model.

Multi-objective optimal scheduling of microgrid with electric vehicles

With the increasing global attention to environmental protection, microgrids with efficient usage of renewable energy have been widely developed. Currently, the intermittent nature of renewable energy and the uncertainty of its demand affect the stable operation of a microgrid. Additionally, electric vehicles (EVs), as an impact load, could severely affect the safe dispatch of the microgrid. To solve these problems, a multi-objective optimization model was established based on the economy and the environmental protection of a microgrid including EVs. The linear weighting method based on two-person zero-sum game was used to coordinate the full consumption of renewable energy with the full bearing of load, and balance the two objectives better. Moreover, the adaptive simulated annealing particle swarm optimization algorithm (ASAPSO) was used to solve the multi-objective optimization model, and obtain the optimal solution in the unit. The simulation results showed that the multi-objective weight method could diminish the influence of uncertainty factors, promoting the full absorption of renewable energy and full load-bearing. Additionally, the orderly charging and discharging mode of EVs could reduce the operation cost and environmental protection cost of the microgrid. Therefore, the improved optimization algorithm was capable of improving the economy and environmental protection of the microgrid.

Intelligent prediction of coal mine water inrush based on optimized SAPSO-ELM model under the influence of multiple factors

Mine water inrush is affected by many factors such as geological structure and fracture zone. However, there may be overlap among these factors, leading to uncertainty, fuzzy similarity and nonlinear relationship among most of them. Therefore, the traditional mathematical model is not ideal to predict water inrush. This paper proposes an intelligent model for predicting water inrush from coal floor based on simulated annealing particle swarm optimization-extreme learning machine (SAPSO-ELM). Based on 144 groups of learning data and 36 groups of predictive validation data, the proposed model extracted common factors from 14 geological factors that might be related to water inrush in a mining area, so as to reduce information interaction among discriminant indexes. In this paper, simulated annealing particle swarm optimization (SAPSO) is innovatively used to optimize the model parameters and compared with other intelligent models (SVM, BPNN, PSO-ELM and ELM) for the learning prediction of the same data. The results show that the common factors extracted from the original variables contain most of the comprehensive information and can reduce information redundancy. Compared with traditional intelligent models (SVM, BPNN, PSO-ELM and ELM), the proposed model improves the computational efficiency of convergence, and the prediction accuracy is higher. It is proved that SAPSO-ELM intelligent algorithm is indeed scientific and has broad application prospect in result prediction induced by complex multi-factors.

Compaction Density Evaluation Model of Sand-Gravel Dam Based on Elman Neural Network With Modified Particle Swarm Optimization

The compaction density of sand-gravel materials has a strong gradation correlation, mainly affected by some material source parameters such as P5 content (material proportion with particle size greater than 5 mm), maximum particle size and curvature coefficient. When evaluating the compaction density of sand-gravel materials, the existing compaction density evaluation models have poor robustness and adaptability because they do not take into full consideration the impact of material source parameters. To overcome the shortcomings of existing compaction density models, this study comprehensively considers the impact of material source parameters and compaction parameters on compaction density. Firstly, asymmetric data were fused and a multi-source heterogeneous dataset was established for compaction density analysis. Then, the Elman neural network optimized by the adaptive simulated annealing particle swarm optimization algorithm was proposed to establish the compaction density evaluation model. Finally, a case study of the Dashimen water conservancy project in China is employed to demonstrate the effectiveness and feasibility of the proposed method. The results show that this model performs high-precision evaluation of the compaction density at any position of the entire working area which can timely correct the weak area of compaction density on the spot, and reduce the number of test pit tests.

Multi-fleet feeder vehicle routing problem using hybrid metaheuristic

In this paper, the Multi-fleet feeder vehicle routing problem (Multi-Fleet FVRP) has been considered. In this problem, trucks and motorcycles have left the depot and served customers. After running out of inventory, at the joint nodes with the trucks, the motorcycles have been reloaded as many as the customers need and finally, returned to the depot. After modeling this problem as a mixed-integer linear programming model, a particle swarm optimization algorithm, as well as a hybrid of particle swarm optimization-simulated annealing (PSO-SA) algorithm, is also proposed. The PSO-SA algorithm employs both PSO and SA sequentially and combines the advantages of PSO’s good exploration capability and SA’s good local search properties. Extensive comparisons have been made to evaluate the performance of the mathematical model and the proposed algorithms using two data groups. The results of small-size instances showed that the outputs of the PSO and PSO-SA algorithms are close to the outputs of the GAMS. Furthermore, these algorithms compared to the ant colony optimization (ACO) and variable neighborhood search (VNS) algorithms in terms of objective function value and runtime have satisfactory results for both data groups. In large-size instances, after tuning the parameters of the hybrid algorithm with the Taguchi method, the results of the proposed algorithm are compared with the PSO, ACO and VNS algorithms. The results and statistical analysis show that the PSO and VNS algorithms have better performance compared to the PSO-SA and ACO algorithms in terms of solution quality. Also, according to the average runtime, the performance of the algorithm PSO-SA is more efficient than that of other algorithms. In general, it can be concluded that the hybrid algorithm has better performance with respect to both time and solution quality criteria.

Multiparameter Identification of Permanent Magnet Synchronous Motor Based on Model Reference Adaptive System—Simulated Annealing Particle Swarm Optimization Algorithm

The accurate identification of permanent magnet synchronous motor (PMSM) parameters is the basis for high-performance drive control. The traditional PMSM multiparameter identification method experiences problems with the uncertainty of the identification results and low identification accuracy due to the under-ranking of the mathematical model of motor control. A multiparameter identification of PMSM based on a model reference adaptive system and simulated annealing particle swarm optimization (MRAS-SAPSO) is proposed here. The algorithm first identifies the electrical parameters of the PMSM (stator winding resistance R, cross-axis inductance L, and magnetic linkage ψf) by means of the model reference adaptive system method. Second, the result is used as the initial population in particle swarm optimization identification to further optimize and identify the electrical and mechanical parameters (moment of inertia J and damping coefficient B) in the motor control system. Additionally, in order to avoid problems such as premature convergence of the particle swarm in the optimization search process, the results of the adaptive simulated annealing algorithm to optimize multiparameter identification are introduced. The simulation experiment results show that the five identification parameters obtained by the MRAS-SAPSO algorithm are highly accurate and stable, and the errors between them and the real values are below 2%. This also verifies the effectiveness and reliability of this identification method.

A Hybrid Latency- and Power-Aware Approach for Beyond Fifth-Generation Internet-of-Things Edge Systems

Fifth-generation (5G) empowered internet of things (IoT) edge networks suffer from latency in delay-sensitive applications. To fulfil the low latency requirements of beyond fifth-generation (B5G)-IoT applications and provide quality of service (QoS) to IoT-edge communication, it is important to minimize server delay. Furthermore, 5G-IoT systems consume more power than their predecessors, which is a concern given the growing size of future IoT networks. This research presents a hybrid latency and power-aware approach for B5G-IoT networks (HLPA B5G-IoT) that minimizes latency with minimum overhead on battery-constrained IoT nodes while simultaneously providing a power-efficient solution for B5G-IoT-edge networks. HLPA B5G-IoT has a novel algorithm classifier tool (ACT) for selecting appropriate optimization algorithms based on the characteristics and requirements of B5G-IoT systems. The ACT matrix not only parametrically compares HLPA B5G-IoT with existing approaches but also identifies crucial parameters that enable algorithm selection for load balancing and energy efficiency. In this paper, metaheuristic algorithms, i.e., biogeography-based optimization (BBO) and grey wolf optimization (GWO), are tailored to meet the requirements of load balancing and power efficiency in IoT-edge systems. The proposed load-balancing algorithm reduces latency and improves overall network performance by 33.33%, 27.45%, 23.52%, 21.56%, 13.72%, 11.76%, and 7.84% compared with simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), bacteria foraging algorithm (BFA), ant colony optimization (ACO), bat algorithm (BA), and genetic SA PSO (GSP), respectively. The power-efficiency algorithm consumes 46.6%, 40%, 32.2%, 27.7%, 15.5%, 11.1%, and 6.6% less energy compared with SA, GA, PSO, BFA, ACO, BA, and GSP, respectively.

Optimal Design of Multimissile Formation Based on an Adaptive SA-PSO Algorithm

In an effort to maximize the combat effectiveness of multimissile groups, this paper proposes an adaptive simulated annealing–particle swarm optimization (SA-PSO) algorithm to enhance the design parameters of multimissile formations based on the concept of missile cooperative engagement. Firstly, considering actual battlefield circumstances, we establish an effectiveness evaluation index system for the cooperative engagement of missile formations based on the analytic hierarchy process (AHP). In doing so, we adopt a partial triangular fuzzy number method based on authoritative assessments by experts to ascertain the weight of each index. Then, considering given constraints on missile performance, by selecting the relative distances and angles of the leader and follower missiles as formation parameters, we design a fitness function corresponding to the established index system. Finally, we introduce an adaptive capability into the traditional particle swarm optimization (PSO) algorithm and propose an adaptive SA-PSO algorithm based on the simulated annealing (SA) algorithm to calculate the optimal formation parameters. A simulation example is presented for the scenario of optimizing the formation parameters of three missiles, and comparative experiments conducted with the traditional and adaptive PSO algorithms are reported. The simulation results indicate that the proposed adaptive SA-PSO algorithm converges faster than both the traditional and adaptive PSO algorithms and can quickly and effectively solve the multimissile formation optimization problem while ensuring that the optimized formation satisfies the given performance constraints.

Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO

In the electricity market environment, the market clearing price has strong volatility, periodicity and randomness, which makes it more difficult to select the input features of artificial neural network forecasting. Although the traditional back propagation (BP) neural network has been applied early in electricity price forecasting, it has the problem of low forecasting accuracy. For this reason, this paper uses the maximum information coefficient and Pearson correlation analysis to determine the main factors affecting electricity price fluctuation as the input factors of the forecasting model. The improved particle swarm optimization algorithm, called simulated annealing particle swarm optimization (SAPSO), is used to optimize the BP neural network to establish the SAPSO-BP short-term electricity price forecasting model and the actual sample data are used to simulate and calculate. The results show that the SAPSO-BP price forecasting model has a high degree of fit and the average relative error and mean square error of the forecasting model are lower than those of the BP network model and PSO-BP model, as well as better than the PSO-BP model in terms of convergence speed and accuracy, which provides an effective method for improving the accuracy of short-term electricity price forecasting.

Critical peak rebate strategy and application to demand response

Time-of-use (TOU) pricing strategy is an important component of demand-side management (DSM), but the cost of supplying power during critical peak periods remains high under TOU prices. This affects power system reliability. In addition, TOU prices are usually applicable to medium- and long-term load control but cannot effectively regulate short-term loads. Therefore, this paper proposes an optimization method for TOU pricing and changes the electricity consumption patterns during the critical peak periods through a critical peak rebate (CPR). This reduces generation costs and improves power system reliability. An optimization model for peak-flat-valley (PFV) period partition is established based on fuzzy clustering and an enumeration iterative technique. A TOU pricing optimization model including grid-side and customer-side benefits is then proposed, and a simulated annealing particle swarm optimization (SAPSO) algorithm is used to solve the problem. Finally, a CPR decision model is developed to further reduce critical peak loads. The effectiveness of the proposed model and algorithm is illustrated through different case studies of the Roy Billinton Test System (RBTS).

A satellite selection algorithm based on adaptive simulated annealing particle swarm optimization for the BeiDou Navigation Satellite System/Global Positioning System receiver

In this article, we propose a new particle swarm optimization–based satellite selection algorithm for BeiDou Navigation Satellite System/Global Positioning System receiver, which aims to reduce the computational complexity of receivers under the multi-constellation Global Navigation Satellite System. The influences of the key parameters of the algorithm—such as the inertia weighting factor, acceleration coefficient, and population size—on the performance of the particle swarm optimization satellite selection algorithm are discussed herein. In addition, the algorithm is improved using the adaptive simulated annealing particle swarm optimization (ASAPSO) approach to prevent converging to a local minimum. The new approach takes advantage of the adaptive adjustment of the evolutionary parameters and particle velocity; thus, it improves the ability of the approach to escape local extrema. The theoretical derivations are discussed. The experiments are validated using 3-h real Global Navigation Satellite System observation data. The results show that in terms of the accuracy of the geometric dilution of precision error of the algorithm, the ASAPSO satellite selection algorithm is about 86% smaller than the greedy satellite selection algorithm, and about 80% is less than the geometric dilution of precision error of the particle swarm optimization satellite selection algorithm. In addition, the speed of selecting the minimum geometric dilution of precision value of satellites based on the ASAPSO algorithm is better than that of the traditional traversal algorithm and particle swarm optimization algorithm. Therefore, the proposed ASAPSO algorithm reduces the satellite selection time and improves the geometric dilution of precision using the selected satellite algorithm.

Transmission ratio optimization of two-speed gearbox in battery electric passenger vehicles

To optimize the power and economic performance of battery electric passenger vehicles, an 8.5-m-long battery electric passenger vehicle was selected as the research subject. The simulated-annealing particle-swarm-optimization (SAPSO) algorithm was applied to optimize the two-speed gearbox and transmission ratio of the vehicle. With the opening of the accelerator pedal and speed as variables, a comprehensive gear shift schedule that considered both the power and economic performance was established. The comprehensive gear shift schedule curve was defined in the model built in the ADVISOR software. The transmission ratio was jointly optimized using the ADVISOR and ISIGHT software, and then the ADVISOR simulation software was used to analyze the economic and dynamic performances corresponding to the optimized transmission ratio. Finally, we compared the power and economic performances of the vehicle before and after transmission ratio optimization, the results of which showed that after the optimization, the maximum speed, climbing gradient, and 0–50 km/h acceleration time of the vehicle were greatly improved, and the driving range was slightly shortened. This enabled performance advantages of the battery electric passenger vehicle by balancing the power and economic performances of the vehicle.

An Application-Specific Approach for Design Structure Matrix Optimization: Focusing on the Cross Application of Modularization and Sequencing Methods

Providing a structured approach for complex product development (PD) is a challenging issue that necessitates utilizing sophisticated modeling tools and analysis techniques. One effective approach for optimizing the complex engineering system development is the cross application of modularization and sequencing analyses on the design structure matrix (DSM), which leads to a cost-effective and time-efficient PD process. In this regard, the current study presents an application-specific method for the simultaneous implementation of modularization and sequencing analyses on DSMs to find the most independent and excluded modules with the minimum feedback loops among them. To this end, we first formulate the total feedback length among the modules by introducing two new modules’ feedback criteria. Second, we propose a novel objective function combining these feedback criteria with the modularity index metric to simultaneously fulfill the sequencing and modularization goals. This objective function makes the proposed method application specific in the sense that the expert is able to adjust the importance weight of each criterion based on the project's characteristics. Third, an efficient metaheuristic algorithm named discrete particle swarm optimization simulated annealing (DPSOSA) is developed to find the optimal solution by minimizing the introduced objective function. Eventually, to evaluate the DPSOSA performance in different applications, we conduct comprehensive experiments on a total of 21 well-known real-world DSMs. The obtained results demonstrate the superiority of the proposed method over previous approaches proving its effectiveness in each of modularization, sequencing, and modularization-sequencing experiments.

Optimization of SMOTE for imbalanced data based on AdaRBFNN and hybrid metaheuristics

Oversampling ratio N and the minority class’ nearest neighboring number k are key hyperparameters of synthetic minority oversampling technique (SMOTE) to reconstruct the class distribution of dataset. No optimal default value exists there. Therefore, it is of necessity to discuss the influence of the output dataset on the classification performance when SMOTE adopts various hyperparameter combinations. In this paper, we propose a hyperparameter optimization algorithm for imbalanced data. By iterating to find reasonable N and k for SMOTE, so as to build a balanced and high-quality dataset. As a result, a model with outstanding performance and strong generalization ability is trained, thus effectively solving imbalanced classification. The proposed algorithm is based on the hybridization of simulated annealing mechanism (SA) and particle swarm optimization algorithm (PSO). In the optimization, Cohen’s Kappa is used to construct the fitness function, and AdaRBFNN, a new classifier, is integrated by multiple trained RBF neural networks based on AdaBoost algorithm. Kappa of each generation is calculated according to the classification results, so as to evaluate the quality of candidate solution. Experiments are conducted on seven groups of KEEL datasets. Results show that the proposed algorithm delivers excellent performance and can significantly improve the classification accuracy of the minority class.

Rule-Based Detection of False Data Injections Attacks against Optimal Power Flow in Power Systems.

Cyber-security of modern power systems has captured a significant interest. The vulnerabilities in the cyber infrastructure of the power systems provide an avenue for adversaries to launch cyber attacks. An example of such cyber attacks is False Data Injection Attacks (FDIA). The main contribution of this paper is to analyze the impact of FDIA on the cost of power generation and the physical component of the power systems. Furthermore, We introduce a new FDIA strategy that intends to maximize the cost of power generation. The viability of the attack is shown using simulations on the standard IEEE bus systems using the MATPOWER MATLAB package. We used the genetic algorithm (GA), simulated annealing (SA) algorithm, tabu search (TS), and particle swarm optimization (PSO) to find the suitable attack targets and execute FDIA in the power systems. The proposed FDIA increases the generation cost by up to 15.6%, 45.1%, 60.12%, and 74.02% on the 6-bus, 9-bus, 30-bus, and 118-bus systems, respectively. Finally, a rule-based FDIA detection and prevention mechanism is proposed to mitigate such attacks on power systems.

Cutting parameter optimization method in multi-pass milling based on improved adaptive PSO and SA

In the production process, cutting parameters greatly affect the production cost and energy consumption, so it is very important for manufacturers to optimize cutting parameters. In this paper, an improved particle swarm optimization (PSO) is presented to optimize cutting parameters for minimizing carbon emissions, production cost and processing time in multi-pass milling. First, a multi-objective optimization model of cutting parameters is established with number of milling passes as one of decision variables. Then, an improved adaptive simulated annealing particle swarm optimization (IAPSOSA) is proposed to obtain the optimal solution of cutting parameters. At last, a case study is given to illustrate that the proposed method is effective to optimize cutting parameters for economic and environmental benefits.