Quantum evolutionary clustering algorithm based on watershed applied to SAR image segmentation

Nuclear reactor reload using Quantum Inspired Algorithm

In this paper, an algorithm is proposed to solve the problem of optimal selected harmonic elimination (SHE) pulse-width modulation (PWM) under a fuzzy constraint that consecutive switching angles are well separated from each other. The problem is first formulated to a fuzzy constrained optimization problem and then a Quantum-inspired Evolutionary Algorithm, which is more efficient than conventional evolutionary algorithms, is applied to approximate the fuzzy solution. The proposed method can effectively reduce switching losses and the probability of switch damages in inverter bridges, and thus can improve the performance of PWM strategy and extend the life of the inverter. This work is the first report on successful application of a Quantum-inspired Evolutionary Algorithm to solve the fuzzy constrained optimization problem.

This paper investigates the problem of minimizing the network coding resources while achieving the desired throughput in a multicast scenario. Since quantum-inspired evolutionary algorithm (QEA), a combination of quantum computing and evolutionary algorithm, can address NP-hard problem and is considered powerful in terms of global optimization, we propose an attempt to adapt QEA to avoid the computational complexity that makes the above problem NP-hard. The effectiveness and the applicability of QEA are demonstrated by carrying out simulation over a number of network topologies.

Quantum-inspired evolutionary algorithm (QEA) has been designed by integrating some quantum mechanical principles in the framework of evolutionary algorithms. They have been successfully employed as a computational technique in solving difficult optimization problems. It is well known that QEAs provide better balance between exploration and exploitation as compared to the conventional evolutionary algorithms. The population in QEA is evolved by variation operators, which move the Q-bit towards an attractor. A modification for improving the performance of QEA was proposed by changing the selection of attractors, namely, versatile QEA. The improvement attained by versatile QEA over QEA indicates the impact of population structure on the performance of QEA and motivates further investigation into employing fine-grained model. The QEA with fine-grained population model (FQEA) is similar to QEA with the exception that every individual is located in a unique position on a two-dimensional toroidal grid and has four neighbors amongst which it selects its attractor. Further, FQEA does not use migrations, which is employed by QEAs. This paper empirically investigates the effect of the three different population structures on the performance of QEA by solving well-known discrete benchmark optimization problems.

This study proposes a novel quantum evolutionary algorithm called four‐chain quantum‐inspired evolutionary algorithm (FCQIEA) based on the four gene chains encoding method. In FCQIEA, a chromosome comprises four gene chains to expand the search space effectively and promote the evolutionary rate. Different parameters, including rotational angle and mutation probability, have been analyzed for better optimization. Performance comparison with other quantum‐inspired evolutionary algorithms (QIEAs), evolutionary algorithms, and different chains of QIEA demonstrates the effectiveness and efficiency of FCQIEA.

An important problem in the study of evolutionary algorithms is how to continuously predict promising solutions while simultaneously escaping from local optima. In this paper, we propose an elitist probability schema (EPS) for the first time, to the best of our knowledge. Our schema is an index of binary strings that expresses the similarity of an elitist population at every string position. EPS expresses the accumulative effect of fitness selection with respect to the coding similarity of the population. For each generation, EPS can quantify the coding similarity of the population objectively and quickly. One of our key innovations is that EPS can continuously predict promising solutions while simultaneously escaping from local optima in most cases. To demonstrate the abilities of the EPS, we designed an elitist probability schema genetic algorithm and an elitist probability schema compact genetic algorithm. These algorithms are estimations of distribution algorithms (EDAs). We provided a fair comparison with the persistent elitist compact genetic algorithm (PeCGA), quantum-inspired evolutionary algorithm (QEA), and particle swarm optimization (PSO) for the 0---1 knapsack problem. The proposed algorithms converged quicker than PeCGA, QEA, and PSO, especially for the large knapsack problem. Furthermore, the computation time of the proposed algorithms was less than some EDAs that are based on building explicit probability models, and was approximately the same as QEA and PSO. This is acceptable for evolutionary algorithms, and satisfactory for EDAs. The proposed algorithms are successful with respect to convergence performance and computation time, which implies that EPS is satisfactory.

Quantum-inspired evolutionary algorithms, one of the three main research areas related to the complex interaction between quantum computing and evolutionary algorithms, are receiving renewed attention. A quantum-inspired evolutionary algorithm is a new evolutionary algorithm for a classical computer rather than for quantum mechanical hardware. This paper provides a unified framework and a comprehensive survey of recent work in this rapidly growing field. After introducing of the main concepts behind quantum-inspired evolutionary algorithms, we present the key ideas related to the multitude of quantum-inspired evolutionary algorithms, sketch the differences between them, survey theoretical developments and applications that range from combinatorial optimizations to numerical optimizations, and compare the advantages and limitations of these various methods. Finally, a small comparative study is conducted to evaluate the performances of different types of quantum-inspired evolutionary algorithms and conclusions are drawn about some of the most promising future research developments in this area.

Based on a quantum-inspired evolutionary algorithm for unit commitment, this paper proposed ways to advance the efficiency and robustness of the algorithm so that its capacity for application in large-scale unit commitment problems can be significantly enhanced. The paper develops an advanced quantum-inspired evolutionary unit commitment algorithm by developing a new initialization method based on unit priority list and a special Q-bit expression for ensuring diversity in the initial search area for improving the efficiency of solution searching. Different techniques such as multi-observation, single-search, and group-search are also proposed for incorporation in the advanced algorithm. The advanced algorithm is tested and compared with the earlier quantum-inspired evolutionary algorithm and a number of known methods through its applications to test systems with up to 100 generator units for a 24-h scheduling horizon.

The path optimization problem with capacity constraints has always been a combinatorial optimization problem, and the quantum evolution algorithm has excellent performance in solving combinatorial optimization problems. Firstly, after analyzing the traditional path optimization problem and the path optimization problem with capacity constraints, the distribution center is numbered 0 based on the customer-based decimal coding method, and the quantum evolution algorithm is improved according to the capacity constraint method. Fixed quantum rotation angle to dynamic rotation angle. The improved algorithm is then applied to vehicle path optimization problems with capacity constraints. Finally, through the MATLAB and LINGO methods to verify the comparison, it can be concluded that the improved quantum evolution algorithm can accurately solve the vehicle path optimization problem, and the improved quantum evolution algorithm has a good influence on the path optimization problem.

For describing nonlinear system accurately and improving adaptive neuro-fuzzy inference system model, a quantum-inspired evolutionary algorithm is presented for optimizing parameters of adaptive neuro-fuzzy inference system. In this paper, an allele real-coded quantum evolutionary algorithm is introduced to optimize the premise and consequent parameters of adaptive neuro-fuzzy inference system. The real-coding method based on allele is adopted, and variable-scale updating strategy of parameter is employed. As a result, the parameter falling into local minimum is avoided. The model accuracy of adaptive neuro-fuzzy inference system is improved. The simulation examples show the effectiveness of proposed method. The proposed improved algorithm is compared with the standard adaptive neuro-fuzzy inference system. Finally, the proposed adaptive neuro-fuzzy inference system model is applied to predict the quality index in textile slashing process.

The quantum-inspired evolutionary algorithm (QEA) uses several quantum computing principles to optimize problems on a classical computer. QEA possesses a number of quantum individuals, which are all probability vectors. They work well for linear problems but fail on problems with strong interactions among variables. Moreover, many optimization problems have multiple global optima. And because of the genetic drift, these problems are difficult for evolutionary algorithms to find all global optima. Local and global migration that QEA uses to synchronize different individuals prevent QEA from finding multiple optima. To overcome these difficulties, we proposed a quantum-inspired evolutionary algorithm with linkage learning (QEALL). QEALL uses a modified concept-guide operator based on low order statistics to learn linkage. We also replaced the migration procedure by a niching technology to prevent genetic drift, accordingly to find all global optima and to expedite convergence speed. The performance of QEALL was tested on a number of benchmarks including both unimodal and multimodal problems. Empirical evaluation suggests that the proposed algorithm is effective and efficient.

This article presents a strategy to solve thermal unit commitment (UC) integrated with an equivalent solar battery system using Fuzzy based Quantum inspired Evolutionary Algorithm (FQEA). As a renewable power source, solar power is injected stochastically with the model. To handle the uncertainty and intermittency involved while integrating solar power and load forecasting, the trivial crisp problem formulations are modified by fuzzification. An evolutionary algorithm based on the concept and principle of quantum computation is applied to solve the UC problem. The conventional Quantum Evolutionary Algorithm (QEA) is advanced by using several operators such as binary differential operator, mutation and crossover along with trivial rotation operator with a re-defined rotational angle look-up table. The QEA is further modified by introducing multi-population based scheme. The fitness function is formulated by combining the objective function, penalty function and the aggregated fuzzy membership function. The proposed FQEA is applied to UC problem in different scaled power systems up to 100 units. Provided simulation results will show the effectiveness of FQEA.

Evolutionary Negative Selection Algorithms (ENSAs) could be regarded as hybrid algorithms of Evolutionary Algorithms (EAs) and Negative Selection Algorithms (NSAs). The average time complexity of ENSAs on combinational optimization problems has never been studied before. In this paper, the average time complexity of ENSAs on one combinational optimization problem is analyzed. The theoretical results demonstrate that, for the Two Max function, the ENSA with an appropriate matching threshold could perform better than the traditional (N+N) EA. Some simulation experiments on the combinational problem are also done, and the experimental results are consistent with theoretical results.

To overcome over-segmentation of Watershed transform, a novel improved Watershed algorithm based on adaptive marker-extraction is proposed. The original marker-based Watershed algorithm is improved by considering multiple feature information of local minima and adaptively selecting threshold. The proposed method consists of five steps: 1) Calculating gradient directly with color vectors; 2) Low-pass filtering of gradient image with BTPF; 3) Employing Hminima transform to extract true local minima whose depth is lower than that of threshold H, which is adaptively adjusted according to gradient image's statistical character. 4) Further marker-extraction being based on water basin scale. 5) Imposing the markers on the original gradient image as its minima; finally, Watershed transform is implied to the marked gradient image to segment the image. Experimental results show that, compared with other testing Watershed algorithms, the proposed method can more efficiently reduce over-segmentation and obtain better segmentation performance with lower computational complexity; in addition, it has better anti-noise performance and edge-location capability as well.

In ordinary statistical methods, multiple outliers in least-squares regression model are detected sequentially one after another, where smearing and masking effects give misleading results. If the potential multiple outliers can be detected simultaneously, smearing and masking effects can be avoided. Such multiple-case outlier detection is of combinatorial nature and 2 N – 1 sets of possible outliers need to be tested, where N is the number of data points. This exhaustive search is practically impossible. Like other combinatorial applications, evolutionary algorithms may play a vital role in multiple-case outlier detection problem. In this paper, we have used Quantum-inspired Evolutionary Algorithm (QEA) for multiple-case outlier detection in least-squares regression model. An information-criterion-based fitness function incorporating extra penalty for number of potential outliers has been used for identifying the most appropriate set of potential outliers. Experimental results with four data sets from statistical literature show that the QEA effectively detects the most appropriate set of outliers.