Simulated Binary Crossover Operator Research Articles

Research on Multi-Objective Process Parameter Optimization Method in Hard Turning Based on an Improved NSGA-II Algorithm

To address the issue of local optima encountered during the multi-objective optimization process with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, this paper introduces an enhanced version of the NSGA-II. This improved NSGA-II incorporates polynomial and simulated binary crossover operators into the genetic algorithm’s crossover phase to refine its performance. For evaluation purposes, the classic ZDT benchmark functions are employed. The findings reveal that the enhanced NSGA-II algorithm achieves higher convergence accuracy and surpasses the performance of the original NSGA-II algorithm. When applied to the machining of the high-hardness material 20MnCrTi, four algorithms were utilized: the improved NSGA-II, the conventional NSGA-II, NSGA-III, and MOEA/D. The experimental outcomes show that the improved NSGA-II algorithm delivers a more optimal combination of process parameters, effectively enhancing the workpiece’s surface roughness and material removal rate. This leads to a significant improvement in the machining quality of the workpiece surface, demonstrating the superiority of the improved algorithm in optimizing machining processes.

Research on the location of shared bicycle parking points based on improved NSGA-II algorithm

Reasonable location selection of shared bicycle parking points is an effective way to solve the current problem of shared bicycles. Considering the constraints such as the amount of shared bicycles, the number of parking points, and the capacity of parking points, a dual-objective bicycle-sharing parking point location planning model considering the cost minimization of both bike-sharing enterprises and travelers is established, and the simulated binary crossover operator and polynomial mutation operator are introduced to improve the NSGA-II algorithm to obtain the Pareto non-inferior solution set that satisfies both bike-sharing enterprises and travelers. The effectiveness of the model and the superiority of the improved algorithm are verified by the case. The results show that the model and algorithm can provide a reference for bicycle sharing enterprises in the location of parking points.

NSGA-II parameterization for the optimal pressure sensor location in water distribution networks

ABSTRACT The optimal location of pressure sensors is typicallysolved using heuristic algorithms. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is one of the most used algorithms in the water industry, requiring a preliminary parameter tuning process. The lack of guidelines on how to tune model parameters generally limits the use of these algorithms by researchers or practitioners and, as such, fails to be used in real-life problems. The current paper explores different NSGA-II parameterizations for the optimal location of pressure sensors by using a multi-objective optimization methodology applied to a real distribution network. Results show that (i) both the uniform and simulated binary crossover operators (depending on the internal parameters) produce the best results, being the former recommended since it does not require further parameter tuning; (ii) polynomial mutation with lower probability value should be chosen; and (iii) the distribution indices of polynomial mutation have a minor effect on NSGA-II performance.

An adaptive adjacent maximum distance crossover operator for multi-objective algorithms

Most genetic operators use random mating selection strategy and fixed rate crossover operator to solve various optimization problems. In order to improve the convergence and diversity of the algorithm, an adaptive adjacent maximum distance crossover operator is proposed in this paper. A new mating selection strategy (distance-based mating selection strategy) and an adaptive mechanism (adaptive crossover strategy based on population convergence) are adopted. Distance-based mating selection strategy purposefully selects parents to produce better offspring. Adaptive crossover strategy based on population convergence increases the convergence speed of the algorithm by controlling the crossover probability. The proposed crossover strategy is evaluated on the simulated binary crossover operators of non-dominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition. The performance of the algorithm is verified on a series of standard test problems. Finally, the optimization results of the improved algorithm using adaptive adjacent maximum distance crossover operator and the standard algorithm are compared and analyzed. The experimental results show that the algorithm using adaptive adjacent maximum distance crossover operator has better optimization results.

A NSGA-II variant for the dynamic economic emission dispatch considering plug-in electric vehicles

The dynamic economic emission dispatch considering plug-in electric vehicles (DEED-PEV) belongs to a nonlinear constrained optimization problem and has the multimodal and discontinuity characteristics. The first characteristic mainly comes from the cost function with valve-point effects, and the second one is associated with the constraints of prohibited operating zones. In this paper, a novel NSGA-II (NNSGA-II) is proposed to deal with DEED-PEV. NNSGA-II replaces the simulated binary crossover operator by the one based on Gaussian distribution, and adaptively adjusts the crossover rate of each individual according to its rank in population. Also, it incorporates a rewarding coefficient into the crowding distance in density estimation, which gives considerations to both density and evenness of solutions. Accordingly, NNSGA-II is able to perform extensive searches in solution space and achieve a series of individuals with high diversity and good evenness. Experimental results suggest that NNSGA-II outperforms the other seven MOEAs for nine DEED-PEV problems with different charging and discharging scenarios of plug-in electric vehicles, and its obtained solutions are superior to those of the other seven MOEAs according to the hypervolume and coverage indicators. In short, NNSGA-II is a potential alternative for minimizing the generation cost and pollutant emission of DEED-PEV.

Multiobjective Load Dispatch for Coal-Fired Power Plants under Renewable-Energy Accommodation Based on a Nondominated-Sorting Grey Wolf Optimizer Algorithm

Coal-fired power plants are widely used to achieve a power balance in grids with renewable energy, which leads to new requirements for speediness in load dispatch. This paper presents a nondominated-sorting grey wolf optimizer algorithm (NSGWO) for the multiobjective load dispatch of coal-fired power plants that employed efficient nondominated sorting, a reference-point selection strategy, and a simulated binary crossover operator. The optimization results of the benchmark functions indicated that the NSGWO algorithm had a better accuracy and a better distribution than the traditional multiobjective grey wolf optimizer algorithm. Regarding the load dispatch of economy, environmental protection, and speediness strategies, the NSGWO had the best performance of all the simulated algorithms. The optimal-compromise solutions of the economy and speediness strategies of the NSGWO algorithm had a good distribution, which elucidated that this novel algorithm was favorable to allowing coal-fired power plants to accommodate renewable energy.

A multi-objective hyper heuristic framework for integrated optimization of carrier-based aircraft flight deck operations scheduling and resource configuration

It is of great significance to produce an efficient flight deck operations scheduling plan for improving the carrier-based aircraft sortie rate and enhancing the combat capability of aircraft carrier formation. Flight deck operations scheduling plan is closely related to flight deck resource configuration. In this paper, in order to find a feasible method to produce an effective operations scheduling plan under different resource configurations, the flight deck operations scheduling problem and resource configuration optimization problem for the pre-flight preparation stage are studied simultaneously. The work is different from the existing literature in three aspects: (1) with considering the transfer time of resources and multiple operation execution modes and analyzing the precedence constraints and resource constraints for the flight deck operations, the integrated optimization model of operations scheduling and resource configuration is established and regarded as a multi-objective optimization model. (2) a novel choice function based multi-objective hyper heuristic is proposed for solving the model. The low level heuristics are three well-known multi-objective evolutionary algorithms, and the heuristic selection strategy is an online learning choice function. (3) in order to further improve the performance of proposed hyper heuristic, two modified hyper heuristics are introduced, one of which uses a simulated annealing-based non-deterministic move acceptance strategy and the other uses a modified initiation method, a simulated binary crossover operator, and a normal distribution sampling mutation operator. By conducting simulation experiments in the case study section, the correctness of the model and the superiority of the multi-objective hyper heuristic framework are verified.

Multi-objective optimization design of anti-rolling torsion bar based on modified NSGA-III algorithm

PurposeHow to get a lighter and stronger anti-rolling torsion bar has become a barrier for the development of high-speed railway vehicles. The purpose of this paper is to realize the multi-objective optimization of an anti-rolling torsion bar with a Modified Non-dominated Sorting Genetic Algorithm III (MNSGA-III), which aims to obtain a better design scheme of an anti-rolling torsion bar device.Design/methodology/approachFirst, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) uses a simulated binary crossover (SBX) operator and a polynomial mutation operator, while the MNSGA-III algorithm proposed in this paper introduces an arithmetic crossover and an adaptive mutation operator to change the crossover and mutate operator in NSGA-III. Second, two algorithms are tested by ZDT3, ZDT4 functions. Both algorithms set the same population size and evolutionary generation, and then compare the results of NSGA-III and MNSGA-III. Finally, MNSGA-III is applied to the multi-objective model of an anti-rolling torsion bar which is established by taking the mass and stiffness of the torsion bar as the optimization object. After that, it obtains the Pareto solution set by solving the multi-objective model with MNSGA-III. The only optimal solution selected from the Pareto solution set is compared with the traditional design scheme of an anti-rolling torsion bar.FindingsThe MNSGA-III converges faster than NSGA-III. Besides, MNSGA-III has better diversity of Pareto solutions than NSGA-III and is closer to the ideal Pareto frontier. Comparing with the results before the optimization, it shows that the volume of the anti-rolling torsion bar reduces by 1.6 percent and the stiffness increases by 3.3 percent. The optimized data verifies the effectiveness of this method proposed in this paper.Originality/valueThe simulated binary crossover operator and polynomial mutation operator of NSGA-III are changed into an arithmetic crossover operator and an adaptive mutation operator, respectively, which improves the optimization performance of the algorithm.

An experimental analysis of a new two-stage crossover operator for multiobjective optimization

Evolutionary algorithms for multiobjective problems utilize three types of operations for progressing toward the higher fitness regions of the search space. Each type of operator contributes in a different way toward the achievement of the common goal. The mutation operation is responsible for diversity maintenance, while the selection operation favors the survival of the fittest. In this paper we focus our attention on the crossover operator. The crossover operator by default is responsible for the search effort and as such deserves our special attention. In particular, we propose a two-stage crossover (TSX) operator for more efficient exploration of the search space. The performance of the proposed TSX operator is assessed in comparison with the simulated binary crossover operator with the assistance of three well-known multiobjective evolutionary algorithms, namely the NSGAII, the SPEA2 and the MOCELL, for the solution of the DTLZ1---7 set of test functions. We also compare the proposed TSX with other popular reproduction operators like the differential evolution and the particle swarm optimization. Finally, we examine the efficacy of the TSX operator in handling problems having five objectives. It is shown with the assistance of the Deb, Thiele, Laumanns and Zitzler set of test functions that the TSX operator can substantially improve the results generated by three popular performance metrics for most of the cases.

An empirical investigation into the effects of chaos on different types of evolutionary crossover operators for efficient global search in complicated landscapes

In this study, a comprehensive empirical test is conducted to analyse the effects of two well-known chaotic maps, namely sinusoidal and logistic maps, on the efficacy of double Pareto crossover, Laplace crossover and simulated binary crossover operators for the global optimization of continuous problems. To do so, 13 well-known numerical benchmark problems in three distinctive dimensions, namely 50D, 100D and 200D, are considered and the genetic algorithm (GA) with simple version and chaos-enhanced versions of the mentioned crossover operators are utilized for optimizing these functions. Furthermore, a time complexity analysis is conducted to find out the impact of hybridizing the chaos and the evolutionary operators on the computational complexity of GA. The results of the experimental analysis provide us with fruitful information regarding the scalability, computational complexity and exploration/exploitation capability of the considered rival optimization algorithms, as well as, demonstrate the efficacy of chaos-evolutionary computing for numerical continuous optimizations.

Particle Swarm Optimization with a Simulated Binary Crossover Operator

Particle swarm optimization (PSO) is a new intelligent search technique, which is inspired by swarm intelligence. Although PSO has shown good performance in many benchmark optimization problems, it suffers from premature convergence in solving complex multimodal problems. In this paper, we propose a novel PSO algorithm, called PSO with a simulated binary crossover operator (SCPSO), to improve the performance of PSO. Experimental results on several benchmark problems show that SCPSO achieves better performance than standard PSO. http://dx.doi.org/10.11591/telkomnika.v12i12.5999

Real-coded Genetic Algorithms with Simulated Binary Crossover Operator.

Real-coded Genetic Algorithms with Simulated Binary Crossover Operator.