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Recent developments in lithium-ion batteries have improved their capacity, which allows them to be used in more applications like power tools. However, they also carry higher risks, such as thermal runaway, which can happen if they are damaged. To make these batteries safer, it is important to improve the design of their housings subjected to multiple drops during their use. This article introduces a new method for optimizing the design of lithium-ion battery housings using a Quantum-Inspired Evolutionary Algorithm (QEA). Previously used mainly in theoretical settings, the authors have adapted QEA for practical engineering tasks. Multiple-drop test simulations were performed, and QEA was used to identify the best housing designs that minimize damage. To support this, a program was developed that automates all drop tests and rebuilds the model. The damage is obtained on the basis of the finite element method (FEM) analyses. The findings show that the algorithm successfully identified designs with the least damage during these tests. This research helps make battery housings safer and explores new uses for QEA in mechanical engineering. [ABSTRACT FROM AUTHOR]

Mobile e-commerce has grown rapidly in the last decade because of the development of mobile network services, computing capabilities and big data's applications. Financial institutions have been undergoing fundamental transformation in credit risk areas, specifically to traditional credit policy, that is now inadequate for accurately evaluating an individual's credit risk profile in a timely manner. A big-scale dataset representing deep mobile usage of 450,722 anonymous mobile users with a 28-month loan history and mobile behavior of both iOS and Android is designed, can add value for credit scoring in terms of better accuracy and lower feature acquisition cost by introducing a cost-based quantum-inspired evolutionary algorithm (QIEA) feature selection method. The QIEA adopts quantum-based individual representation and quantum rotation gate operator to improve feature exploration capability of conventional genetic algorithm (GA). The expected feature yield fitness function introduced in QIEA able to identify cost-effective feature subsets. Experimental results show that quantum-based method achieves good predictive performances even with only 70–80% number of features selected by GAs, and hence achieve lower feature acquisition costs with budget constraints. Additionally, computational time can be reduced by 30–60% compared with GAs depending on different feature set sizes. [ABSTRACT FROM AUTHOR]

Recent developments in lithium-ion batteries have improved their capacity, which allows them to be used in more applications like power tools. However, they also carry higher risks, such as thermal runaway, which can happen if they are damaged. To make these batteries safer, it is important to improve the design of their housings subjected to multiple drops during their use. This article introduces a new method for optimizing the design of lithium-ion battery housings using a Quantum-Inspired Evolutionary Algorithm (QEA). Previously used mainly in theoretical settings, the authors have adapted QEA for practical engineering tasks. Multiple-drop test simulations were performed, and QEA was used to identify the best housing designs that minimize damage. To support this, a program was developed that automates all drop tests and rebuilds the model. The damage is obtained on the basis of the finite element method (FEM) analyses. The findings show that the algorithm successfully identified designs with the least damage during these tests. This research helps make battery housings safer and explores new uses for QEA in mechanical engineering.

This paper proposed a novel distributed memetic evolutionary model, where four modules distributed exploration, intensified exploitation, knowledge transfer, and evolutionary restart are coevolved to maximize their strengths and achieve superior global optimality. Distributed exploration evolves three independent populations by heterogenous operators. Intensified exploitation evolves an external elite archive in parallel with exploration to balance global and local searches. Knowledge transfer is based on a point-ring communication topology to share successful experiences among distinct search agents. Evolutionary restart adopts an adaptive perturbation strategy to control search diversity reasonably. Quantum computation is a newly emerging technique, which has powerful computing power and parallelized ability. Therefore, this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionary algorithm, referred to as quantum-inspired distributed memetic algorithm (QDMA). In QDMA, individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum hyperspace. The QDMA integrates the superiorities of distributed, memetic, and quantum evolution. Computational experiments are carried out to evaluate the superior performance of QDMA. The results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon’s rank-sum test. The superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model, but also to superior designs of each special component.

It is often computationally intensive to solve combinatorialoptimisation problems due to the inherent large solution space. These problems are commonly observed in the fields of engineering system design and operations. Traditional techniques are limited in handling the growing complexity and size of these problems efficiently. This paper presents a twofold update quantum-inspired genetic algorithm to solve combinatorial optimisation problems. It is generalised as an improved version of quantum-inspired evolutionary algorithm. The paper proposes a new problem formulation and the solution procedure for quantum-inspired evolutionary algorithms. An improved quantum-inspired genetic algorithm is proposed with a twofold update mechanism along with various operators. The proposed method is applied to solving a real-life engineering system optimisation problem of modular design. The results are compared using a classical genetic algorithm versus a quantum-inspired evolutionary algorithm, indicating that the proposed method outperforms the traditional methods and is more robust and more efficient. [ABSTRACT FROM AUTHOR]

In order to improve the average throughput performance of edge users of heterogeneous networks, an optimization scheme of downlink interference coordination in heterogeneous cellular network based on non-orthogonal multiple access (NOMA) was proposed. Cell range expansion (CRE) and almost blank subframe (ABS) technology were applied to inter-cell interference coordination and the improved quantum-inspired evolutionary algorithm (IQEA) was used to realize the joint optimization scheme of adaptive CRE and ABS. The simulation results show that NOMA grouping can effectively improve the performance of edge users in heterogeneous networks. Compared with the scheme without interference coordination, the average throughput of edge users of the traditional scheme with non-adaptive interference coordination and the proposed optimization scheme with adaptive interference coordination is increased by 4.93 dB and 5.48 dB, respectively. The average throughput of network users is increased by 0.96 dB and 1.01 dB, respectively, and the logarithmic utility sum of network users is increased by 0.13 dB and 0.14 dB, respectively. [ABSTRACT FROM AUTHOR]

This work proposes the utilization of the quantum-inspired evolutionary algorithm (QEA) for user pairing in non-orthogonal multiple access (NOMA). By exploiting quantum concepts such as superposition, it obtained a user pairing solution that approximates the highest achievable sum rate. Moreover, elitist QEA (E-QEA) is proposed to further enhance the performance by eliminating the risk of losing the best solution of the current iteration in the next iteration. Simulation result demonstrates that E-QEA and QEA yield higher average achievable sum rates compared to random user pairing.

Edge caching and adaptive bitrate video streaming are two promising techniques to ensure a good video viewing experience. Edge caching can bring contents closer to users to alleviate redundant content transmissions, reduce user-perceived delay and improve transmission capability. Adaptive bitrate video streaming is able to adaptively adjust video quality based on time-varying network conditions and different users’ preference. Due to the strong coupled relationship between caching and user association, in this article, we focus on the issue of joint caching and user association optimization for adaptive bitrate video streaming in UAV-assisted cellular networks. First, we formulate the optimization problem as a non-linear integer programming (NLIP) to minimize the content delivery delay. To solve this challenging NP-hard problem, a heuristic algorithm based on quantum-inspired evolutionary algorithm (QEA) is proposed to obtain the best caching and user association solutions iteratively. Finally, simulations are conducted to demonstrate that compared with three benchmark algorithms without joint optimization of caching and user association, the proposed algorithm can greatly improve users’ video viewing experience and achieve better system performance in terms of reducing the total content delivery delay.

This paper proposes a quantum-inspired evolutionary algorithm (QiEA) to solve an optimal service-matching task-assignment problem. Our proposed algorithm comes with the advantage of generating always feasible population individuals and, thus, eliminating the necessity for a repair step. That is, with respect to other quantum-inspired evolutionary algorithms, our proposed QiEA algorithm presents a new way of collapsing the quantum state that integrates the problem constraints in order to avoid later adjusting operations of the system to make it feasible. This results in lower computations and also faster convergence. We compare our proposed QiEA algorithm with three commonly used benchmark methods: the greedy algorithm, Hungarian method and Simplex, in five different case studies. The results show that the quantum approach presents better scalability and interesting properties that can be used in a wider class of assignment problems where the matching is not perfect. [ABSTRACT FROM AUTHOR]

The Resource-Constrained Project-Scheduling Problem (RCPSP) is an NP-hard problem which can be found in many research domains. The optimal solution of the RCPSP problems requires a balance between exploration/exploitation and diversification/intensification. With this in mind, quantum-inspired evolutionary algorithms' ability to improve the population and quality of solutions, this work investigates the performance of a quantum-inspired genetic algorithm (QIGA), which has been adapted to work with RCPSPs. The proposed QIGA possesses the same structure as classical genetic algorithms, but the initial and updated populations are implemented using quantum gates and quantum superposition, bearing in mind the adaptation of such operators to fit with discrete problems. This work aims to solve RCPSPs using the QIGA to investigate the influence of the various quantum parameters in the proposed algorithm, such as the quantum population size, different options for the quantum gates and re-combination to use in the QIGA. The well-known PSPLIB benchmark instances of J30, J60 and J120 activities are used to test the effectiveness and performance of the proposed QIGA. It is apparent from the results that quantum mutation, quantum crossover and representation of quantum superposition using quantum gates enhances population diversity. The QIGA is also found to outperform many other evolutionary algorithms.

This paper proposes a quantum-inspired evolutionary algorithm (QiEA) to solve an optimal service-matching task-assignment problem. Our proposed algorithm comes with the advantage of generating always feasible population individuals and, thus, eliminating the necessity for a repair step. That is, with respect to other quantum-inspired evolutionary algorithms, our proposed QiEA algorithm presents a new way of collapsing the quantum state that integrates the problem constraints in order to avoid later adjusting operations of the system to make it feasible. This results in lower computations and also faster convergence. We compare our proposed QiEA algorithm with three commonly used benchmark methods: the greedy algorithm, Hungarian method and Simplex, in five different case studies. The results show that the quantum approach presents better scalability and interesting properties that can be used in a wider class of assignment problems where the matching is not perfect.

A monitoring system is an essential part of power system operation and planning. This system model is used to observe the system stability behaviour, to detect the unforeseen power system instabilities and to prevent the system from being collapsed. Since the power system scheme is a complex and non-linear system, employing the conventional method based on a mathematical model for designing a monitoring system is quite complicated. This paper proposes an advanced wide-area monitor design using a machine learning method called a multi-output least-square support vector machine to overcome the problem above. The application of the kernel approach in a multi-output least-square support vector machine provides higher accuracy in predicting result and flexibility. The kernel technique will transform the input data into a higher dimensional feature subspace as an input data of a multi-output least-square support vector machine. Then this non-linear pattern will be transformed into a linear pattern and intensive computing time can also be reduced. The major problem of multi-output least-square support vector machine parameters is on finding the best parameter. If the trial and error method is used to find the parameter of multi-output least-square support vector machine parameters, the machine is ineffective in obtaining good performance for a wide range of operating conditions and various load change scenarios in a multi-area power system. A quantum-inspired evolutionary algorithm is employed to achieve the machine's appropriate parameters automatically and accurate output by minimizing the objective function. In order to measure the rigorousness of multi-output least-square support vector machine output, mean square error is used as an objective function in this study. The simulation results show that the amalgamation of the multioutput least-square support vector machine and quantum-inspired evolutionary algorithm provides a satisfactory result when compared to standard multi-output least-square support vector machine output. In addition, this proposed method can be considered as a promising technique in the future as a wide-area monitor model. Copyright © 2020 Praise Worthy Prize S.r.l. - All rights reserved. [ABSTRACT FROM AUTHOR]

The density classification task is one among other benchmark problems for studying the ability of cellular automata to solve problems through emergent collective computations. The density classification task attempts to find a local rule that can perform majority voting in an arbitrary initial configuration of a cellular automaton. Solutions for this problem were designed by means of several training mechanisms, more particularly optimization algorithms, due to the lack of standard procedures for selecting suitable local rules. In this paper, we propose new investigations into density determination in one-dimensional cellular automata of radius r, 4. We show that our proposal allows retaining a considerable number of unknown rules, some of which may outperform the current efficient solutions. Moreover, we give explanations about the computational mechanisms making global computations emerge so that the considered problem is solved. This is a key element for improving both the general understanding of the way in which computational tasks are solved by emergence and the selection of suitable local state-transitions rules. [ABSTRACT FROM AUTHOR]

Manufacturing resources are shared and centrally managed on the cloud platform in cloud manufacturing, which is a new model of modern manufacturing. The production data are collected, which can be used to predict the manufacturing events. Based on those, disruption problems of scheduling should be researched from a new point of view. In this paper, new job arrivals were considered as the disruption event. The time of the occurrence of disruption was predictable in contrast to uncertainty. Alternative subcontractors chosen from the cloud platform were available for outsourcing with different processing prices and transporting distances. The objective of the original scheduling, the deviation between the new schedule and the old one, and the outsourcing cost were all considered. To express the problem, mathematical models and a three-field notation model were constructed. To solve the problem, a hybrid quantum-inspired chaotic group leader optimization algorithm was proposed, in which a hybrid encoding way was applied. To verify the algorithm, experiments were carried out. The results showed that the proposed algorithm performs well.

Abstract Quantum-inspired Evolutionary Algorithm (QEA) is a kind of intelligent algorithm which widely and effectively used in many fields. In QEA, the basic and common operations usually include quantum chromosome observation and quantum gate update. Quantum rotation gate (QRG) is the most commonly used operator for the operation of quantum gate update, which has a significant influence on the performance of QEA. Many kinds of QRGs have been proposed with different methods to set the only parameter of QRG, i.e., rotation angle. In this paper, a study on classification of QRG is first conducted with respect to rotation direction and magnitude of rotation angle by analyzing and summarizing various kinds of QRGs in literature, and then the corresponding definitions, descriptions and analyses are presented. Furthermore, in order to investigate and compare performances of different QRGs, we set 21 kinds of QRG schemes based on the classification of rotation direction and magnitude of rotation angle. Four typical complex function optimization problems and a 0–1 knapsack problem are selected as experiment objects to test the 21 kinds of schemes. Comprehensive processing and analyzing for the experiment data are conducted, which draws some valuable conclusions for the more reasonable and more effective applications of QEA. Highlights • A classification on quantum rotation gate (QRG) is conducted. • Comparative analyses on different QRGs are carried out. • Rotation Scheme III performs better than others in most cases. • The value of 0.05π seems to be a recommendable static angle amplitude. • Dynamic angle amplitudes perform better than static angle amplitudes on the whole. [ABSTRACT FROM AUTHOR]

This paper proposes an optimally robust H∞ polynomial fuzzy controller design using quantum-inspired evolutionary algorithm (QEA) for continuous/discrete time polynomial fuzzy systems with model uncertainties and external disturbances. To improve control performance, QEA is adopted to evolve optimal control gains with a fitness function that is defined by performance requirements. The stability and robustness of the control system are then guaranteed by the proposed robust H∞ stability conditions, which are formed by the sum of squares (SOS) method. By using the principle of copositivity, novel relaxed SOS-based stability conditions are derived to reduce the conservativeness of solving SOS-based stability conditions, while the feasible solution space is broadened. Four numerical examples demonstrate the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

It had been proved that the knowledge may promote more efficient evolution. Considering the knowledge defined in different form, we present interval multi-objective quantum-inspired cultural algorithms so as to effectively utilize the implicit information embodied in the evolution to promote more efficient search. It adopts the dual structure derived from cultural algorithm. In population space, the rectangle’s height of each allele in real-encoding quantum individuals is calculated in terms of the possibility dominant rank, instead of the relative fitness values. Three kinds of crowding operators are defined, including the crowding distance of hypercube, the harmonic distance of hypercube and the coverage rate of hypercube to grids, to measure the crowding degree among evolutionary individuals. In belief space, the knowledge is used to guide selection and mutation operations of evolutionary individuals and the update operation of quantum individuals. The statistic simulation results for four benchmark functions indicate that the solutions obtained from the proposed algorithms more close to the true Pareto front uniformly and the uncertainty of non-dominant solutions is less. Furthermore, the knowledge extracted from the evolution plays a positive role in improving the convergence and distribution. [ABSTRACT FROM AUTHOR]

Process planning and job shop scheduling problems are the two classical but crucial activities in manufacturing system. With the approach of integrated process planning and scheduling, the two actual activities are combined to conduct operation selection and operation sequencing with the constraints of practical job shop status. In this article, a quantum-inspired hybrid algorithm with the objective of minimum makespan is proposed, aiming to solve integrated process planning and scheduling problems in dynamic manufacturing systems. A hybrid-coding representation is suggested, which is a three-layer structure in numerical representation and Q-bit representation adopted from quantum-inspired evolutionary algorithm. Based on the hybrid-coding representation, customized converting and repairing rules and methods are presented to generate feasible individuals. Q-gate rotation and group leader optimization algorithm are integrated systematically for the population evolution to accelerate the convergence speed of the proposed algorithm. In order to increase the diversity of population, a chaotic map called logistic map is introduced, bringing the stochastic initial individuals. Experiments show that the proposed hybrid algorithm can generate outstanding outcomes for integrated process planning and scheduling instances. [ABSTRACT FROM AUTHOR]

Aiming at three-echelon location-routing problem (3E-LRP) with directed graph theory, a systematic model is built up, and a combination intelligent algorithm of quantum-inspired evolutionary algorithm (QEA) and genetic algorithm (GA) is applied to solve it. The QEA is applied to solve facility location problem (FLP) and facility allocation problem (FAP), and the GA is applied to solve vehicle routing problem (VRP). The GA optimizes the VRP according to the parameters from QEA, and returns the result of VRP to QEA. The QEA works in coordination with GA, and the 3E-LRP is solved by the combination of QEA and GA. In order to improve the efficiency of the algorithm, searching strategy based on reachable distribution region and facility allocation strategy based on path length are proposed in this paper. The example calculation shows that the mathematical model and the combined intelligent algorithm can effectively solve the 3E-LRP, which provides the theory and method guidance for ME-LRP. [ABSTRACT FROM AUTHOR]

Recent advances lead to the increase in the capability of evolutionary algorithms for tacking optimization problems. Particularly quantum-inspired computation leads to a new direction for enhancing the effectiveness of these algorithms. Existing studies on quantum-inspired algorithms focused primarily on evolving a single set of homogeneous solutions. This paper expands the scope of current research by applying quantum computing principles, in particular the quantum superposition principle proposing a new quantization approach. As a result, a quantum-inspired competitive coevolution algorithm (QCCEA) is proposed in this paper. Unlike its predecessors and traditional quantum-inspired algorithms, the proposed QCCEA uses a novel approach for quantization using Gaussian distribution which alters the selection procedure in order to identify the optimal fitness. This new approach which incorporates a new qubit architecture and processing has proved effective for numerical optimization problems as well as multiobjective problems. Experiments were performed on 20 benchmark numerical optimization problems as well as a combinatorial maze problem. The experiment results show that the proposed quantization technique has improved the performance and accuracy of the traditional non-quantized algorithms significantly for the both types of problems. This paper further discusses the impact of population before and after quantization as well as sensitivity of the parameters. The novel approach of quantization that improved the efficiency and performance is the key contribution of this work. [ABSTRACT FROM AUTHOR]

The world around us may be viewed as a network of entities interconnected via their social, economic, and political interactions. These entities and their interactions form a social network. A social network is often modeled as a graph whose nodes represent entities, and edges represent interactions between these entities. These networks are characterized by the collective latent behavior that does not follow trivially from the behaviors of the individual entities in the network. One such behavior is the existence of hierarchy in the network structure, the sub-networks being popularly known as communities. Discovery of the community structure in a social network is a key problem in social network analysis as it refines our understanding of the social fabric. Not surprisingly, the problem of detecting communities in social networks has received substantial attention from the researchers. In this paper, we propose parallel implementations of recently proposed community detection algorithms that employ variants of the well-known quantum-inspired evolutionary algorithm (QIEA). Like any other evolutionary algorithm, a quantum-inspired evolutionary algorithm is also characterized by the representation of the individual, the evaluation function, and the population dynamics. However, individual bits called qubits, are in a superposition of states. As chromosomes evolve individually, the quantum-inspired evolutionary algorithms (QIEAs) are intrinsically suitable for parallelization. In recent years, programmable graphics processing units — GPUs, have evolved into massively parallel environments with tremendous computational power. NVIDIA® compute unified device architecture (CUDA®) technology, one of the leading general-purpose parallel computing architectures with hundreds of cores, can concurrently run thousands of computing threads. The paper proposes novel parallel implementations of quantum-inspired evolutionary algorithms in the field of community detection on CUDA-enabled GPUs. The proposed implementations employ a single-population fine-grained approach that is suited for massively parallel computations. In the proposed approach, each element of a chromosome is assigned to a separate thread. It is observed that the proposed algorithms perform significantly better than the benchmark algorithms. Further, the proposed parallel implementations achieve significant speedup over the serial versions. Due to the highly parallel nature of the proposed algorithms, an increase in the number of multiprocessors and GPU devices may lead to a further speedup. [ABSTRACT FROM AUTHOR]

Manufacturing resources are shared and centrally managed on the cloud platform in cloud manufacturing, which is a new model of modern manufacturing. The production data are collected, which can be used to predict the manufacturing events. Based on those, disruption problems of scheduling should be researched from a new point of view. In this paper, new job arrivals were considered as the disruption event. The time of the occurrence of disruption was predictable in contrast to uncertainty. Alternative subcontractors chosen from the cloud platform were available for outsourcing with different processing prices and transporting distances. The objective of the original scheduling, the deviation between the new schedule and the old one, and the outsourcing cost were all considered. To express the problem, mathematical models and a three-field notation model were constructed. To solve the problem, a hybrid quantum-inspired chaotic group leader optimization algorithm was proposed, in which a hybrid encoding way was applied. To verify the algorithm, experiments were carried out. The results showed that the proposed algorithm performs well. [ABSTRACT FROM AUTHOR]

The flexible job shop scheduling problem (FJSP) is vital to manufacturers especially in today's constantly changing environment. It is a strongly NP-hard problem and therefore metaheuristics or heuristics are usually pursued to solve it. Most of the existing metaheuristics and heuristics, however, have low efficiency in convergence speed. To overcome this drawback, this paper develops an elitist quantum-inspired evolutionary algorithm. The algorithm aims to minimise the maximum completion time (makespan). It performs a global search with the quantum-inspired evolutionary algorithm and a local search with a method that is inspired by the motion mechanism of the electrons around atomic nucleuses. Three novel algorithms are proposed and their effect on the whole search is discussed. The elitist strategy is adopted to prevent the optimal solution from being destroyed during the evolutionary process. The results show that the proposed algorithm outperforms the best-known algorithms for FJSPs on most of the FJSP benchmarks. [ABSTRACT FROM AUTHOR]

In this paper, a new solution is proposed for the engineering problem that designing accurate holes lay-out could be very hard by 3D laser detecting in complex goaf with irregular boundary and ore pillars. Some detecting holes should be pre-designed on the horizontal characteristic sectional image which is derived from the data of the history and the preliminary geophysical prospecting of the goaf, and the optimal solution can be worked out according to the system evaluation function with the quantum-inspired evolutionary algorithm (QEA). In the optimal solution, some factors such as the irregular boundary of the outline of the goaf, possible ore pillars with irregular boundary and the valid range of the laser probes as well as the economic benefit should also be taken into consideration to establish a general optimization model. The results of practical operation and statistical analysis show that the optimal model proposed in this paper is more effective in efficiency and accuracy than genetic algorithm (GA) which can provide the optimal solution in a relatively short time and make sure the solution work successfully. Thus, it helps to make scientific decision in detecting complex goaf with 3D laser. [ABSTRACT FROM AUTHOR]

Quantum-inspired evolutionary algorithms (QIEAs) combine the advantages of quantum-inspired bit ( Q-bit), representation and operators with evolutionary algorithms for better performance. Using quantum-inspired representation the complete binary search space can be generated by collapsing a single Q-bit string repeatedly. Thus, even a population size of 1 can be taken in a QIEA implementation resulting in enormous saving in computation. Although this is correct in theory, QIEA implementations run into trouble in exploring large search spaces with this approach. The Q-bit string has to be initialized to produce each possible binary string with equal probability and then altered slowly to probabilistically favor generation of strings with better fitness values. This process is unacceptably slow when the search spaces are very large. Many ideas have been reported with EAs/QIEAs for speeding up convergence while ensuring that the algorithm does not get stuck in local optima. In this paper, the possible features are identified and systematically introduced and tested in the QIEA framework in various combinations. Some of these features increase the randomness in the search process for better exploration and the others compensate by local search for better exploitation together enabling a judicious combination tailored for particular problem being solved. This is referred to as 'right-sizing the randomness' in the QIEA search. Benchmark instances of the well-known and well-studied Quadratic Knapsack Problem are used to demonstrate how effective these features are-individually and collectively. The new framework, dubbed QIEA-QKP, is shown to be much more effective than canonical QIEA. The framework can be utilized with profit on other problems and is being attempted. [ABSTRACT FROM AUTHOR]

The problem of contention for routers and links is one of the critical problems caused by the NoC (network-on-chip) reuse approach in testing NoC systems. In this paper, we propose zigzag connected subgraph partition approach to avoid the contention for routers and links in NoC testing. Then, a MMQEA (multi-population multi-nary quantum-inspired evolutionary algorithm) strategy, which incorporates multi-population and multi-nary techniques, is presented to solve the test scheduling problem for NoC. In addition, power constraint is taken into consideration. Experimental results for the ITC'02 benchmarks show that the new approach results in a reduction in test time, compared to previous work. [ABSTRACT FROM AUTHOR]

Evacuation planning contains more than a few decisions which have to be made in a very short period of time and in the most appropriate way. Evacuation path optimization has vital importance in reducing the human and social harm and saving the aid time. Significant research efforts have been made in the literature to deal with evacuation optimization on the basis of deterministic optimization model, nevertheless the stochastic aspects or uncertainty of real-world evacuation have not been taken into account comprehensively. Inspired by the promising performance of heuristic algorithms to solve combinatorial problems, this paper proposes an improved quantum ant colony algorithm (QACA) for exhaustive optimization of the evacuation path that people can evacuate from hazardous areas to safe areas. In comparison with ACO (ant colony optimization) based method, QACA has the capability of finding a good solution faster using fewer individuals and possesses strong robustness, as a result of the quantum representation and updating of pheromone. Experiment results show that the proposed approach executes more effectively during evacuation. [ABSTRACT FROM AUTHOR]

The adaptive niche quantum-inspired immune clonal algorithm (ANQICA) is proposed by combining the quantum coding, immune clone and niche mechanism together to solve the multi-modal function optimization more effectively and make the function converge to as many as possible extreme value points. The quantum coding can better explore the solution space, the niche mechanism ensures the algorithm to converge to multi-extremum and the adaptive mechanism is introduced according to the characteristics of each procedure of the algorithm to improve the effect of the algorithm. Example analysis shows that the ANQICA is better in exploration and convergence. Therefore, the ANQICA can be used to solve the problem of multi-modal function optimization effectively. [ABSTRACT FROM AUTHOR]

As an important way of addressing energy and environmental challenges, the market share of wind power generation has increased dramatically in the past decade and has introduced significant challenges to power system operation. In this article, the tail correlation between multiple wind farms is studied. The joint probability distribution of multiple wind farms is estimated by employing the Gumbel copula function. Based on the estimated joint probability distribution, a stochastic optimal dispatch model is proposed to take into account the chance constraints of energy utilization from multiple wind farms in the power system. The sample average approximation method is employed to handle the chance constraints in the proposed model, so as to transform stochastic optimal dispatch into a deterministic non-linear optimization problem. The quantum-inspired evolutionary algorithm is used to solve the proposed model. The proposed model and algorithm are tested with comprehensive case studies to demonstrate their effectiveness. [ABSTRACT FROM AUTHOR]

It is attractive to reuse the on-chip functional interconnects as test access mechanism (TAM) in network-on-chip (NoC) system testing. However, in the methodology of NoC-reuse as TAM, the influence factors in NoC testing significantly increased. To further reduce test time and show significant gains over other work, we propose XY-direction connected subgraph partition (XYCSP) approach to eliminate the path conflicts before testing, and concurrently determine the position of test access points. We then present a multiple test clock strategy to bridge the gap between the NoC channel bandwidth and the core test wrapper bandwidth. With the help of adaptive probability gate quantum-inspired evolutionary algorithm (APGQEA) strategy, which blends adaptive strategy and multi-nary oriented techniques, the proposed NoC test scheduling algorithm permits quick exploration and exploitation of the solution space. Moreover, power constraints are also taken into account. Experimental results for the ITC'02 benchmarks show that the proposed scheme can achieve shorter test time compared to prior works. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid algorithm called QDEPSO (Quantum inspired Differential Evolution with Particle Swarm Optimization) which combines differential evolution (DE), particle swarm optimization method (PSO) and quantum-inspired evolutionary algorithm (QEA) in order to solve the 0-1 optimization problems. In the initialization phase, the QDEPSO uses the concepts of quantum computing as the superposition state of qubits as well as the quantum measurement to represent and generate the diversity of the initial solutions. The second phase is an alternation between the DE operations (mutation, crossover and selection) and the adaptation of update formula of the velocity and the position of PSO algorithm. The effect of this step is to determine the rotation quantum angle to explore search space of solutions. To evaluate the performance of the proposed algorithm, we use the knapsack 0-1 problem as a class of combinatorial optimization NP-hard problems. The obtained results for 0-1 knapsack problem have proven the superior performance of QDEPSO compared to Quantum-inspired Evolutionary algorithm (QEA), Adaptive Quantum-inspired Differential Evolution Algorithm (AQDE), Quantum Swarm Evolutionary algorithm (QSE) and Quantum Inspired Harmony Search Algorithm (QIHSA). [ABSTRACT FROM AUTHOR]

This paper proposes an approximate optimization approach, called QEAM, which combines a P system with active membranes and a quantum-inspired evolutionary algorithm. QEAM uses the hierarchical arrangement of the compartments and developmental rules of a P system with active membranes, and the objects consisting of quantum-inspired bit individuals, a probabilistic observation and the evolutionary rules designed with quantum-inspired gates to specify the membrane algorithms. A large number of experiments carried out on benchmark instances of satisfiability problem show that QEAM outperforms QEPS (quantum-inspired evolutionary algorithm based on P systems) and its counterpart quantum-inspired evolutionary algorithm. [ABSTRACT FROM AUTHOR]

Quantum-inspired evolutionary algorithm has premature and slow convergence shortcomings on solving numerical optimization problems. To overcome these shortcomings, a novel quantum-inspired evolutionary algorithm based on culture & knowledge is proposed by introducing the cultural algorithm. This algorithm contains two evolutionary layers: quantum evolutionary layer and knowledge evolutionary layer. Since the introduction of cultural algorithm, this algorithm can achieve fine balance between exploration and exploitation as well as can escape from local optimum. Because of the new framework and quantum observation, the proposed algorithm not only retains the advantages of quantum coding, but also effectively solves numerical optimization problems. The experimental results show that the algorithm has better performance than the quantum-inspired evolutionary algorithms. The proposed algorithm performs better than other related algorithms in terms of speed and accuracy. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel quantum-inspired evolutionary algorithm (QEA), called quantum inspired Tabu search (QTS). QTS is based on the classical Tabu search and characteristics of quantum computation, such as superposition. The process of qubit measurement is a probability operation that increases diversification; a quantum rotation gate used to searching toward attractive regions will increase intensification. This paper will show how to implement QTS into NP-complete problems such as 0/1 knapsack problems, multiple knapsack problems and the traveling salesman problem. These problems are important to computer science, cryptography and network security. Furthermore, our experimental results on 0/1 knapsack problems are compared with those of other heuristic algorithms, such as a conventional genetic algorithm, a Tabu search algorithm and the original QEA. The final outcomes show that QTS performs much better than other heuristic algorithms without premature convergence and with more efficiency. Also on multiple knapsack problems and the traveling salesman problem QTS verify its effectiveness. [ABSTRACT FROM AUTHOR]

A membrane-inspired evolutionary algorithm (MIEA) is a successful instance of a model linking membrane computing and evolutionary algorithms. This paper proposes the analysis of dynamic behaviors of MIEAs by introducing a set of population diversity and convergence measures. This is the first attempt to obtain additional insights into the search capabilities of MIEAs. The analysis is performed on the MIEA, QEPS (a quantum-inspired evolutionary algorithm based on membrane computing), and its counterpart algorithm, QIEA (a quantum-inspired evolutionary algorithm), using a comparative approach in an experimental context to better understand their characteristics and performances. Also the relationship between these measures and fitness is analyzed by presenting a tendency correlation coefficient to evaluate the importance of various population and convergence measures, which is beneficial to further improvements of MIEAs. Results show that QEPS can achieve better balance between convergence and diversity than QIEA, which indicates QEPS has a stronger capacity of balancing exploration and exploitation than QIEA in order to prevent premature convergence that might occur. Experiments utilizing knapsack problems support the above made statement. [ABSTRACT FROM AUTHOR]

Abstract: The short-term hydrothermal generation scheduling (SHGS) problem considered valve-point effects and transmission losses is a complicated problem which has the characteristics of nonlinear and non-convex. This paper presents a clonal real-coded quantum-inspired evolutionary algorithm (CRQEA) with Cauchy mutation for solving SHGS problem. In this proposed algorithm, real-coded rule is adopted for handling continuous variables. Meanwhile, clonal operator and Cauchy mutation are integrated into the proposed algorithm to avoid premature convergence. Moreover, heuristic strategies are designed for dealing with all kinds of constraints effectively. Finally, three test systems are employed to verify the feasible and effectiveness the proposed CRQEA. Compared with other reported methods, the obtained results clearly illustrates that CRQEA has better performances. [Copyright &y& Elsevier]

This paper presents a membrane algorithm, called MAQIS, by appropriately combining concepts and principles of membrane computing and quantum-inspired evolutionary approach. MAQIS has four distinct features from the membrane algorithms reported in the literature: initial solutions are only inside the skin membrane; different regions separated by membranes have different components of the algorithm; all the components inside membranes cooperate to produce offspring in a single evolutionary generation; communication rules are performed in a single evolutionary step. Extensive experiments conducted on knapsack problems show that MAQIS outperforms five counterpart approaches and our previous work. The effectiveness of MAQIS is also verified in the application of image processing. [ABSTRACT FROM AUTHOR]