Quantum-inspired Immune Clonal Algorithm Research Articles

Quantum-inspired immune clonal algorithm for railway empty cars optimization based on revenue management and time efficiency

By proposing the concept of timeline, transform dynamic vehicle scheduling problem into a series of static vehicle scheduling problems. With the objective function of benefit maximization, the cloud preference model of dynamic empty car scheduling is built considering empty car delay time constraint. The non-dominated antibodies are proportionally immune clonal according to their cloud preference, which are defined by their cloud application preferences. It is beneficial to enhance the forecasting accuracy of the immune gene manipulation, and to increase the speed of finding the optimal solution based on the application preference. Experimental results conclusively demonstrate the efficiency and effectiveness of the improving system availability, load balancing deviation and valid time brought by the proposed algorithm in cloud computing environments, conditions and that more close to the reality, empty car scheduling model for specific time was established.

Quantum-Inspired Immune Clonal Algorithm for solving large-scale capacitated arc routing problems

In this paper, we present an approach to Large-Scale CARP called Quantum-Inspired Immune Clonal Algorithm (QICA-CARP). This algorithm combines the feature of artificial immune system and quantum computation ground on the qubit and the quantum superposition. We call an antibody of population quantum bit encoding, in QICA-CARP. For this encoding, to control the population with a high probability evolution towards a good schema we use the information on the current optimal antibody. The mutation strategy of quantum rotation gate accelerates the convergence of the original clone operator. Moreover, quantum crossover operator enhances the exchange of information and increases the diversity of the population. Furthermore, it avoids falling into local optimum. We also use the repair operator to amend the infeasible solutions to ensure the diversity of solutions. This makes QICA-CARP approximating the optimal solution. We demonstrate the effectiveness of our approach by a set of experiments and by Comparing the results of our approach with ones obtained with the RDG-MAENS and RAM using different test sets. Experimental results show that QICA-CARP outperforms other algorithms in terms of convergence rate and the quality of the obtained solutions. Especially, QICA-CARP converges to a better lower bound at a faster rate illustrating that it is suitable for solving large-scale CARP.

A flexible and generalized framework for access network selection in heterogeneous wireless networks

The rapid development and integration of heterogeneous wireless networks provide ubiquitous communications for mobile users. The intelligent and multimodal mobile terminals should select the best access network at any time anywhere. However, the “best” is a complex and fuzzy concept, which has different meanings to different users and even to the same user under different conditions. There are various factors to consider when deciding which one is the best for a mobile terminal. In this paper, we design a generalized and flexible framework for the access network selection in heterogeneous wireless networks. The framework is generalized because it considers various factors in a comprehensive way to get the solutions. These factors can be classified as network-related or user-related, economic or non-economic, objective or subjective, accurate or fuzzy. Meanwhile, the framework is also flexible because these factors can be customized and adapted to specific solutions. Under the framework, given Nmobile terminals and M access networks, we have developed a novel access network selection scheme based on a Quantum-inspired Immune Clonal Algorithm (QICA). Experimental results demonstrate that our proposed scheme provides better utilities for both the users and the access networks, and also better services for users as compared with four other schemes.

Adaptive niche quantum-inspired immune clonal algorithm

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.

Change detection for SAR images based on quantum-inspired immune clonal clustering algorithm

As the conventional evolutionary clustering optimization methods are often time-consuming and easy to trap in local optimal value in dealing with the problem of change detection.Furthermore,it can not detect the edge accurately for SAR images.We proposed a method for change detection in SAR images based on the clustering analysis.The proposed method takes gray-levels as an input,uses the quantum bit to define the clustering center,searches the optimal cluster center using the quantum-inspired immune clonal algorithm,and gets the global threshold.Finally,the change-detection map is produced.Compared with KI threshold,it can achieve a better value.Compared with Genetic Algorithm Based Clustering(GAC),the proposed method can search a much better clustering center quickly and effectively.Besides,it can detect the accurate edge and improve the change detection accuracy.

A novel quantum-inspired immune clonal algorithm with the evolutionary game approach

The quantum-inspired immune clonal algorithm (QICA) is a rising intelligence algorithm. Based on evolutionary game theory and QICA, a quantum-inspired immune algorithm embedded with evolutionary game (EGQICA) is proposed to solve combination optimization problems. In this paper, we map the quantum antibody’s finding the optimal solution to player’s pursuing maximum utility by choosing strategies in evolutionary games. Replicator dynamics is used to model the behavior of the quantum antibody and the memory mechanism is also introduced in this work. Experimental results indicate that the proposed approach maintains a good diversity and achieves superior performance.

Quantum-Inspired Immune Clonal Algorithm for Global Optimization

Based on the concepts and principles of quantum computing, a novel immune clonal algorithm, called a quantum-inspired immune clonal algorithm (QICA), is proposed to deal with the problem of global optimization. In QICA, the antibody is proliferated and divided into a set of subpopulation groups. The antibodies in a subpopulation group are represented by multistate gene quantum bits. In the antibody's updating, the general quantum rotation gate strategy and the dynamic adjusting angle mechanism are applied to accelerate convergence. The quantum not gate is used to realize quantum mutation to avoid premature convergences. The proposed quantum recombination realizes the information communication between subpopulation groups to improve the search efficiency. Theoretical analysis proves that QICA converges to the global optimum. In the first part of the experiments, 10 unconstrained and 13 constrained benchmark functions are used to test the performance of QICA. The results show that QICA performs much better than the other improved genetic algorithms in terms of the quality of solution and computational cost. In the second part of the experiments, QICA is applied to a practical problem (i.e., multiuser detection in direct-sequence code-division multiple-access systems) with a satisfying result.