Quantum-inspired evolutionary algorithm with linkage learning

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.

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.

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.

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.

Autoimmune hepatitis (AIH) is an inflammatory liver disease with an undiscovered cause that is attributed to the promiscuous humoral as well as cellular immune response against homologous self-antigens. If AIH is not diagnosed and treated in its early stages, it can result in cirrhosis or liver failure. In this regard, we propose a novel algorithm based on fuzzy cognitive maps (FCM) for paving the way for accurate diagnosis of it. For doing so, major and innate characteristics of AIH which play a significant role in diagnosing it, in addition to the data of 216 samples-suffering from AIH-have been gathered by the help of three pathologists. Then, we have applied our developed FCM solution on obtained data in order to classify them in one the definite AIH or improbable AIH classes. Our devised algorithm utilizes quantum inspired evolutionary algorithm (QEA) as a link reduction tool as well as particle swarm optimization algorithm as a link tuning mean. In the QEA, instead of coding the presence and absence of links between concepts with 1 and 0, respectively, the probability of their existence or inexistence is modeled with a Q-bit (the smallest information unit in the QEA) and, depending on the outcome of objective function, the quantum state of these Q-bits are updated. Using a probabilistic representation instead of 0 and 1, in addition to creating diversity in the solution space, can lead to escapes from many local optima; which is an issue of concern in the optimization of FCM structure. Experimental results show that not only does our developed algorithm make accurate diagnosis, but it outperforms other conventional methods as well.

Nuclear reactor reload using Quantum Inspired Algorithm

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.

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.

Many metaheuristic algorithms have been proposed to solve combinatorial and numerical optimization problems. Most optimization problems have high dependence, meaning that variables are strongly dependent on one another. If a method were to attempt to optimize each variable independently, its performance would suffer significantly. When traditional optimization techniques are applied to highdependence problems, they experience difficulty in finding the global optimum. To address this problem, this paper proposes a novel metaheuristic algorithm, the entanglement-enhanced quantum-inspired tabu search algorithm (Entanglement-QTS), which is based on the quantum-inspired tabu search (QTS) algorithm and the feature of quantum entanglement. Entanglement-QTS differs from other quantum-inspired evolutionary algorithms in that its Q-bits have entangled states, which can express a high degree of correlation, rendering the variables more intertwined. Entangled Q-bits represent a state-of-the-art idea that can significantly improve the treatment of multimodal and high-dependence problems. Entanglement-QTS can discover optimal solutions, balance diversification and intensification, escape numerous local optimal solutions by using the quantum not gate, reinforce the intensification effect by local search and entanglement local search, and manage strong-dependence problems and accelerate the optimization process by using entangled states. This paper uses nine benchmark functions to test the search ability of the entanglement-QTS algorithm. The results demonstrate that Entanglement-QTS outperforms QTS and other metaheuristic algorithms in both its effectiveness at finding the global optimum and its computational efficiency.

Quantum computers are seen as the next generation computing technique with the processing power potential they have. However, currently, quantum computers are limited in terms of hardware and algorithmic capabilities. In this study, quantum-inspired methods which are formed by combining quantum computation techniques with classical algorithms are focused on. It has been emphasized in many studies that quantum-inspired methods provide advantages especially for metaheuristic methods. Different from them, in this study, the performance of population-based quantum-inspired methods are compared. The paper focuses on solving the same optimization problem by using quantum-inspired versions of the population-based optimization algorithms such as evolutionary algorithm, genetic algorithm, and differential evolution algorithm. The experimental results show that, while Quantum-inspired Evolutionary Algorithm is better at global search, Quantum-inspired Differential Evolution Algorithm is better at local search and more accurate results.

Quantum-inspired evolutionary algorithms (QIEAs), as a subset of evolutionary computation, are based on the principles of quantum computing such as quantum bits and quantum superposition. In this paper, we propose a multi-start quantum-inspired evolutionary algorithm, called MSQIEA. To improve the performance of the algorithm, a multi-measurement operator and a new strategy for updating the rotation angle is proposed. When Q-bit individuals start to converge to their final states, the best solution is stored and all Q-bits in each Q-bit individual are reinitialized. We compare the effectiveness of MSQIEA with a popular quantum-inspired evolutionary algorithm, called QEA, for solving 0-1 knapsack problem. The experimental results show that MSQIEA outperforms QEA and finds a solution with higher profit.

Quantum rotation gate in quantum-inspired evolutionary algorithm: A review, analysis and comparison study

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