Solving Fuzzy Job-shop Scheduling Problems by a Hybrid Optimization Algorithm

There are many uncertain factors in job shop scheduling problems. However, those uncertainties are critical for the scheduling procedures. The imprecise processing times are modeled as triangular fuzzy numbers (TFNs) and the due dates are modeled as trapezium fuzzy numbers in this paper. A multi-objective genetic algorithm is proposed to solve fuzzy job shop scheduling problems, in which the objective functions are conflicting. Agreement index (AI) is used to show the satisfaction of client which is defined as value of the area of processing time membership function intersection divided by the area of the due date membership function. The multi-objective function is composed of maximize both the minimum agreement and maximize the average agreement index. Two benchmark problems were used to show the effectiveness of the proposed approach. Experimental results demonstrate that the multi-objective genetic algorithm does not get stuck at a local optimum easily, and it can solve job-shop scheduling problems with fuzzy processing time and fuzzy due date effectively.

A hybrid algorithm based on particle swarm and chemical reaction optimization for multi-object problems

Fuzzy job shop scheduling problem with availability constraints

Chemical-reaction optimization for flexible job-shop scheduling problems with maintenance activity

The parallel machine scheduling problems exist in many real-life industrial examples. In order to effectively utilize the machines and decline the total waiting time of jobs assigned to each machine, the maximum total completion time per machine on a parallel machine is thus addressed. Due to the NP-hardness of the problem under consideration, in this paper the authors explore an optimal solution by a branch-and-bound method, incorporating several dominances and a lower bound. In addition, the authors also adopted an iterated local search (ILS) and a Tabu search to find its near-optimal solution for the problem. The computational results on randomly generated instances illustrate that the proposed Tabu search algorithm performs better than the two existing algorithms, and the ILS method for the small jobs, but the ILS method performs better than the two existing algorithms and Tabu search algorithm for the bigger jobs.

Reliable computer modelling in hydromechanical system dynamics is very important for studies related to optimizing food manufacturing and biotechnology processes. Searching best parameters of a computational model of a system and its diagnosing are formulated as inverse problems. The current error function is built on base of comparison of corresponding parts of the computed eigenspectrum and the measured one. The incompleteness of the registered data and the presence of multiple eigenfrequencies lead to the fact that the error function to be minimized is nonconvex and not everywhere differentiable. Since the function has many local minima, it is necessary to use global optimization methods. Two novel hybrid global optimization algorithms for solving the inverse problems are described. The hybrid algorithms combine the up-to-date stochastic algorithm for scanning the search space and deterministic derivative-free procedures for local descent. Numerical results on benchmarking the hybrid algorithm are presented to illustrate the efficiency of the approach.

The calibration accuracy of the accelerometer, a key device in an inertial navigation system, directly affects the navigation accuracy. In our study, a novel calibration method for a triaxial accelerometer was presented with automatic online calibration. A robotic arm was used to set different orientations for the accelerometer. The error parameters, including the scale factor and bias, were estimated using a particle swarm optimization (PSO) and differential evolution (DE) hybrid algorithm with adjustable inertia weight. The effectiveness of the new algorithm was validated using simulated data with or without noise in simulation. Simulation results showed that the hybrid algorithm increased the measurement accuracy by many orders of magnitude and outperformed the single PSO and DE algorithms in terms of convergence speed and global searchability. The new algorithm was applied to a real accelerometer experiment. The experiment results demonstrate that proper calibration parameters can be obtained without a precise turntable.

In this paper we compare different heuristic methods for the manufacturing cell formation problem considering part process sequence: a GRASP algorithm, a reactive GRASP algorithm and a hybrid algorithm which combines reactive GRASP and tabu search. All algorithms are tested with a set of instances from the literature. The results from the GRASP algorithm are compared to those of the reactive GRASP in order to evaluate the advantages of automatically adjusting the parameter value within the randomized greedy procedure. Also the reactive GRASP results are compared to those of the hybrid algorithm to evaluate the contribution to solution quality of replacing the local search phase of the GRASP algorithm with tabu search.

BackgroundProtein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task.ResultsIn order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods.ConclusionsThe hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.

This paper is concerned with the problem of robust optimization method for coupling parametrization design of space observation system. The objective is to develop an robust optimization framework to design space observation system. An improved robust collaborative optimization (IRCO) method for complex system design using the hybrid optimization algorithm is developed, which associates genetic algorithm (GA) with tabu search (TS). The proposed method can avoid unsolvability or converging difficulty and handle uncertainty within and across disciplines more efficiently and reasonably. The results demonstrate the effectiveness and applicability of the proposed method.

The rapid development in the wearable sensors helped develop the healthcare-based Internet of Things (IoT) platforms for continuous monitoring of chronic disease. As per the World Health Organization (WHO) 85% of the Cardiovascular Disease (CVD) is preventable with proper monitoring for symptoms and awareness. The electrocardiogram (ECG) signal refers to a recording of electrical activity that is generated by the human heart. An analysis of the ECG signal helps in building models that can make an automatic classification of various CVDs. Deep neural network is found to be efficient to make an automatic classification of the primary signals of the ECG. These ECG signals to the arrhythmia classes were grouped by using the Convolutional Neural Networks (CNN). The 9 Layers of the CNN techniques are employed in state-of-the-art modern research to classify the signal. The Tabu Search (TS) algorithm with the CNN, Hybrid Optimization Algorithm known as the CNN with ABC-TS, and CNN with Artificial Bee Colony (ABC) is used to optimize the CNN in this work for classifying ECG signals. An evaluation of the technique was made on the basis of metrics of performance evaluation that are capable of producing enhanced outcomes.

Solution method for the location planning problem of logistics park with variable capacity

A new hybrid algorithm named TTGA hybrid algorithm which means genctic and tabu hybrid algorithm (TGA) with optimized tent mapping is proposed in this paper, through the research of tabu search(TS), genetic algorithm (GA)and chaotic algorithm(COA)[1].Based on evolutionary group generated by GA, auxiliary group formed by optimized tent mapping are ledinto group through specific selection mechanism so that the group become more diverse and effective. And in the meanwhile the tabu list is used to add a memory so that a similarity judgment mechanism is set based on the same individuals among group which provides a basis for using the TS operator to realize local fine search, so the tabu search plays the role of avoiding roundabout. In the new algorithm the introduction of TS ensures the capacity of hill climbing and refined search while the diversity of population and the effectiveness of evolution is assured by the regularity and ergodicity of chaotic. In this paper GA, IGA and TGA and TTGA is used in reactive power optimization of power grid, the results show that TTGA has the best performance on convergence and overall search.

In this manuscript, a hybrid cheetah and artificial rabbits optimisation algorithm fostered digital control systems for micro electro mechanical systems gyroscope (MEMS-GYS-HCO-ARO) is proposed. In this method, the design procedure starts by designing a closed loop control systems for the micro electro mechanical systems gyroscope and loop parameters are optimised using hybrid algorithms. Initially, the loop parameters are optimised using HCO and ARO. Then, the dwarf mongoose optimisation algorithm and LMS demodulator are utilised for demodulating the noise signal. The performance of the proposed MEMS-GYS-HCO-ARO method attains amplitude analysis of 39.25%, 33.36% and 32.99% of lower amplitude compared with existing methods, like a design of micro electro mechanical systems gyroscope using hybrid optimisation algorithm (MEMS-GYS-HOA), digital control and readout of MEMS gyroscope with second-order sliding mode control (MEMS-GYS-SOSMC) and a digital closed loop sense MEMS disk resonator gyroscope circuit design based on integrated analogue front-end (DCLS-MEMS-IAF) respectively.

A newly hybrid nature-inspired algorithm called HSSGWOA is presented with the combination of the salp swarm algorithm (SSA) and grey wolf optimizer (GWO). The major idea is to combine the salp swarm algorithm's exploitation ability with the grey wolf optimizer's exploration ability to generate both variants' strength. The proposed algorithm uses to tune the parameters of the integral sliding mode controller (ISMC) that design to improve the dynamic performance of the two-link flexible joint manipulator. The efficiency and the capability of the proposed hybrid algorithm are evaluated based on the selected test functions. It is clear that when compared to other algorithms like SSA, GWO, differential evolution (DE), gravitational search algorithm (GSA), particles swarm optimization (PSO), and whale optimization algorithm (WOA). The ISMC parameters were tuned using the SSA, which was then compared to the HSSGWOA algorithm. The simulation results show the capabilities of the proposed algorithm, which gives an enhancement percentage of 57.46% compared to the standard algorithm for one of the links, and 55.86% for the other.