Discrete Crossover Research Articles

基于离散人工蜂群算法的农业机器人丘陵地区农田遍历路径规划

In this study, the discrete artificial bee colony (DABC) algorithm was proposed to plan the path of agricultural robots traversing multiple fields in hilly areas. Based on the basic ABC algorithm as the framework, the path coding method was adopted, and the discrete crossover operator, reverse operator, immune operator, and single/multi-step 2-opt operator were comprehensively used to help hired bees, observing bees, and scout bees to generate new food sources. Finally, the optimized field traversal order and the entrance and exit distribution of each field were obtained. The simulation results showed that compared with the traditional ABC algorithm, the average shortest path of the DABC algorithm proposed in this study was shortened by 1.59%, accompanied by the less iterations contributing to algorithm convergence and good ability to jump out of the local optimal solution. The simulation experiment was carried out using real field data and field operation parameters. The field traversal order and the entrance and exit distribution obtained by the proposed method can effectively reduce the length of the transfer path and its repeatability. This study exhibits superiority and feasibility in the field traversal path planning of agricultural robots in hilly areas, and the trajectory coordinates output by the algorithm can provide a path reference for large-area operations of agricultural machinery drivers or unmanned agricultural machineries.

Neighborhood-based particle swarm optimization with discrete crossover for nonlinear equation systems

Solving nonlinear equation systems (NESs) is important in engineering and science. In general, NESs have more than one root, and it is a great challenge to locate multiple roots in a single run for numerical computation. In order to tackle this challenging task, we propose a neighborhood-based particle swarm optimization with discrete crossover (MNPSO). MNPSO has three main characteristics: (1) it adopts a two-stage framework to balance exploitation and exploration well; (2) a distance-based adaptive neighborhood technique is proposed, which can form an appropriate neighborhood for each particle; (3) a discrete crossover operator improves the performance of PSO in the high dimension problems. We choose thirty NESs with different features as the test suite to evaluate the performance of MNPSO. The experimental results reveal that MNPSO can locate multiple roots in a single run, and the root ratio and the success ratio of MNPSO are higher than other methods.

A hybrid algorithm for integrated scheduling problem of complex products with tree structure

Due to the poor design of encoding methods or evolutionary operators in previous genetic-algorithm-based integrated scheduling algorithms, this paper proposes an integrated scheduling algorithm based on a hybrid genetic algorithm and tabu search. Firstly, an encoding method based on a dynamic schedulable operation set is proposed. This method cannot only reflect the priority constraints among operations, but also avoid the problems of imposing constraints and missing solution space in previous division encoding method. Secondly, a decoding method based on machine idle time driving is presented to handle the scheduling order of operations on different machines. Then, two different discrete crossover and mutation operators are designed to ensure the legitimacy of the processing sequence of the same machine. Finally, a local search strategy based on tabu search is shown to enhance the search capability for superior solutions. The algorithm is tested by the randomly generated instances, and the experimental results indicate that the proposed algorithm is effective and can achieve satisfactory performance.

An Efficient Meta-Heuristic for Multi-Objective Flexible Job Shop Inverse Scheduling Problem

In reality, uncertainties may still encounter after a scheduling scheme is generated. These may make the original schedule non-optimal or even impossible. Traditional scheduling methods are not effective in dealing with these situations. In response to this phenomenon, by introducing the idea of inverse optimization into the scheduling field, a new scheduling strategy called “inverse scheduling” has been proposed. To the best of our knowledge, this is the first study to be conducted on flexible job shop inverse scheduling problem (FJISP). In this paper, first, a comprehensive mathematical model with adjustable processing time is established. Then, a hybrid multi-objective evolutionary algorithm based on decomposition and particle swarm optimization is adopted for solving FJISP. To make the proposed algorithm solving FJISP more efficiently, some new strategies are used. A 3-D coding scheme is employed to represent the particles, multiple strategies are designed for generating a high-quality initial population, and effective discrete crossover and mutation operators are specially designed according to the FJISP’s characteristics. Finally, computational experiments are carried out using extended benchmarks, and the results demonstrate the effectiveness of the proposed algorithm for solving the FJISP.

A MEMS Implementation of the Classic Meissner Parametric Resonator: Exploring High-Order Windows of Unbounded Response

In recent years, increasing attention is given to parametric resonators, in which an unbounded response may be induced by periodically modulating one of the system parameters. Parametric resonators with a single degree of freedom have many windows of unbounded response in which the response frequency is different from the modulation frequency. This difference between input and output frequencies may be relevant to sensing applications. In this paper, we explore the solution map of the Meissner parametric resonator, and we experimentally characterize odd-ordered windows of unbounded response, up to the 15th window. Another interesting feature of the Meissner resonator is discrete crossover points in which an unbounded response is possible at distinct modulation frequencies and distinct modulation amplitudes. In this paper, we experimentally characterize one such crossover point. Our parametric resonator is based on an electrostatic anti-spring, which allows to modulate stiffness that is otherwise unaffected by motion. This is in contrast to many previous experimental studies in which the modulated stiffness was affected by motion and thus included unwanted nonlinear effects. [2016-0209]

OPTIMAL POWER FLOW EVALUATION OF POWER SYSTEM USING GENETIC ALGORITHM

This paper presents an efficient genetic algorithm for solving non-convex optimal power flow (OPF) problems with bus voltage constraints for practical application. In this method, the individual is the binary-coded representation that contains a mixture of continuous and discrete control variables, and crossover and mutation schemes are proposed to deal with continuous/discrete control variables, respectively. The objective of OPF is defined that not only to minimize total generation cost but also to improve the bus voltage profile.. The proposed method is demonstrated for a IEEE 30-bus four generator ystem, and it is compared with conventional method.The experimental results show that the GA OPF method is superior to the conventional.

Design and Implementation of an Effective Fuzzy Logic Controller based on Quantum Inspired Evolutionary Algorithm

This paper proposes a new approach based on quantum inspired evolutionary algorithm (QIEA) for effective selection and definition of fuzzy if-then control rules as well as the shapes of membership functions (MFs) to design fuzzy logic controllers (FLCs). The majority of works done on designing FLCs rely on the knowledge base derived from imprecise heuristic knowledge of experienced operators or persons. These traditional methods, however, are cumbersome to implement and very time consuming to evaluate. Our proposed approach is a self-learning adaptive method and decomposes a problem in such a way that leads to more effective knowledge acquisition and improved control performance with the FLCs. In order to verify the effectiveness of this self-learning adaptive method, a standard test-bed, the truck backer-upper problem, is considered as the test problem. During each generation, the rules are updated and the MFs’ parameters are altered using a complementary double mutation operator (CDMO) and a discrete crossover (DC). This paper also demonstrates the effect of different fuzzification and defuzzification methods on the response of the FLC. The center of gravity (COG) and modified COG are used as defuzzifier to analyze the results of the fuzzy controller. The experimental results show that the proposed approach with different fuzzification and MCOG to design FLCs performs better than the traditional methods with triangular fuzzification and COG in terms of required time to backing up the truck.

Equivalent properties of monolayer fabric from mesoscopic modelling strategies

A general and systematic approach for the development of mesostructurally-based continuum model of woven fabrics has been elaborated, relating the fabric behavior at the macroscopic continuum scale to the response and geometry of the fabric’s mesostructure (geometrical configuration of the weave and the yarn properties). Mesoscopic discrete models of dry fabric have been developed based on a discretization of the yarn geometry, accounting for the yarn–yarn interactions at the yarns crossing points. The yarns are modeled within a unit cell consisting of the repetitive fabric pattern as curved planar beams submitted to the reaction forces of the transverse yarns at discrete crossover points. Those reaction forces are expressed in semi-analytical form versus the yarn geometry and mechanical properties for general armour from beam theory. The equilibrium shape of the woven fabric is obtained by minimizing its total potential energy, accounting for the work of the reaction forces due to the transverse yarns. The absolute minimum of the structure’s total potential energy is achieved by a classical genetic algorithm. Simulation results show that plain weave presents a nonlinear response in the early deformation stage due to the crimp change, whereas twill shows a quasi linear response due to yarn extension being the dominant deformation mechanism. Plain weave fabric overall exhibits an orthotropic constitutive law, as biaxial simulations show. The transverse behavior of plain weave fabric is presently evaluated in terms of Poisson’s ratio, based on virtual simulations at the mesoscopic scale of analysis. Simulation results show that Poisson’s ratio first increases towards a maximum due to the rapid shrinkage of the sample in the transverse direction, and decreases thereafter when the crimp changes become limited by the reaction forces of the transverse yarns. The influence of the mechanical properties of both warp and weft on Poisson’s coefficient is assessed. The predictions of the mesoscopic models regarding the impact of yarn geometry and mechanical properties on the overall behavior provide a guideline for the design of woven fabrics.

Chaotic ant swarm for the traveling salesman problem

Chaotic ant swarm (CAS) is an optimization algorithm based on swarm intelligence theory, which has been applied to find the global optimum solution in continuous search space. However, it is not able to solve the combinational optimization problem directly, e.g., the traveling salesman problem (TSP). To tackle this problem, we propose a new method to solve the traveling salesman problem based on chaotic ant swarm, CAS-TSP for short. The CAS-TSP is developed by introducing a mapping from continuous space to discrete space, reverse operator and crossover operator into the CAS. Computer simulations demonstrate that the CAS-TSP is capable of generating optimal solution to instances of the TSPLIB in almost all test problems of sizes up to 150. Also a comparative computational study shows that this CAS-TSP algorithm is an efficient tool for solving TSP and this heuristic is competitive also with other heuristics.

Random fuzzy chance-constrained programming based on adaptive chaos quantum honey bee algorithm and robustness analysis

This paper proposes an adaptive chaos quantum honey bee algorithm (CQHBA) for solving chance-constrained programming in random fuzzy environment based on random fuzzy simulations. Random fuzzy simulation is designed to estimate the chance of a random fuzzy event and the optimistic value to a random fuzzy variable. In CQHBA, each bee carries a group of quantum bits representing a solution. Chaos optimization searches space around the selected best-so-far food source. In the marriage process, random interferential discrete quantum crossover is done between selected drones and the queen. Gaussian quantum mutation is used to keep the diversity of whole population. New methods of computing quantum rotation angles are designed based on grads. A proof of convergence for CQHBA is developed and a theoretical analysis of the computational overhead for the algorithm is presented. Numerical examples are presented to demonstrate its superiority in robustness and stability, efficiency of computational complexity, success rate, and accuracy of solution quality. CQHBA is manifested to be highly robust under various conditions and capable of handling most random fuzzy programmings with any parameter settings, variable initializations, system tolerance and confidence level, perturbations, and noises.

Fuzzy job shop scheduling problem with availability constraints

This paper presents the fuzzy job shop scheduling problem with availability constraints. The objective is to find a schedule that maximizes the minimum agreement index subject to periodic maintenance, non-resumable jobs and fuzzy due-date. A random key genetic algorithm (RKGA) is proposed for the problem, in which a novel random key representation, a new decoding strategy incorporating maintenance operation and discrete crossover ( DX) are used. RKGA is applied to some fuzzy scheduling problem with availability constraints and compared with other algorithms. Computational results show that RKGA performs better than other algorithms.

Solving fuzzy job shop scheduling problems using random key genetic algorithm

This paper addresses job shop scheduling problems with fuzzy processing time and fuzzy trapezoid or doublet due date. An efficient random key genetic algorithm (RKGA) is suggested to maximize the minimum agreement index and to minimize the maximum fuzzy completion time. In RKGA, a random key representation and a new decoding strategy are proposed and two-point crossover (TPX) and discrete crossover (DX) are considered. RKGA is applied to some fuzzy scheduling instances and performance analyses on random key representation, and the comparison between RKGA and other algorithms are done. Computation results validate the effectiveness of random key representation and the promising advantage of RKGA on fuzzy scheduling.

PROBABILISTIC ADAPTIVE CROSSOVER (PAX): A NOVEL GENETIC ALGORITHM CROSSOVER METHODOLOGY

A new crossover technique for genetic algorithms is proposed in this paper. The technique is called probabilistic adaptive crossover and denoted by PAX. The method includes the estimation of the probability distribution of the population, in order to store in a unique probability vector P information about the best and the worse solutions of the problem to be solved. The proposed methodology is compared with six crossover techniques namely: one-point crossover, two-point crossover, SANUX, discrete crossover, uniform crossover and selective crossover. These methodologies were simulated and compared over five test problems described by ONEMAX Function, Royal Road Function, Random L-MaxSAT, Bohachevsky Function, and the Himmelblau Function.