Quantuminspired evolutionary algorithms for optimization problems this repository contains some unpublished before source codes developed by robert nowotniak in the years 20102015. A multiobjective quantuminspired genetic algorithm moqiga for. In this study, quantuminspired methods which are formed by combining. Quantuminspired evolutionary algorithm qea has been designed by integrating some quantum mechanical principles in the framework of evolutionary algorithms. In this paper, a new variant of a quantuminspired evolutionary algorithm is proposed, which is characterised by a populationbased elitism, a resetting mutation for the qubits, and an. It is noted that the parameters of the artificial neural network. Quantuminspired evolutionary approach for the quadratic.
Quantuminspired evolutionary algorithm for travelling salesman. Quantum inspired evolutionary algorithm qea is a new optimization technique which has combined quantum computing principles with evolutionary algorithms. The novel contribution of the proposed lsqea is the use of a qga to explore the optimal feasible region in. Solving combinatorial optimization problems with quantum inspired evolutionary algorithm tuned using a novel heuristic method nija mani, gursaran, and ashish mani nija mani is with department of mathematics, dayalbagh educational institute deemed university, dayalbagh, agra, india email. Everything you always wanted to know about quantum. Computers free fulltext quantum genetic algorithms. Quantum inspired genetic algorithm knowledge engineering. Hence, it is a typical multiobjective optimization problem. This paper describes a realvalued quantuminspired evolutionary algorithm qiea, a new computational approach which bears similarity with estimation of distribution algorithms edas. Evolutionary algorithms for the solution of software requirements.
Objectiveto analyse the efficacy of quantuminspired elitist multiobjective evolutionary algorithm qemea, quantuminspired multiobjective differential evolution algorithm qmdea and multiobjective quantuminspired hybrid differential evolution mqhde in. In 2010 ying 10 proposed that quantum computing could be used to achieve certain goals in artificial intelligence ai. Effect of population structures on quantuminspired. It is based on the concept and principles of quantum computing, such as quantum bit and superposition of states, whose features are. List of quantuminspired algorithms theoretical computer. This quantuminspired acromyrmex evolutionary algorithm qiaea proposal falls within the rapidly growing emergent field of qieas that have shown to. The proposed quantuminspired evolutionary algorithm qea has applications in various combinatorial optimization problems in power systems and elsewhere. The book explores this emerging field of research that applies principles of quantum mechanics to develop more efficient and robust intelligent systems. Request pdf quantuminspired evolutionary algorithm for travelling. They have been successfully employed as a computational technique in solving difficult optimization problems.
This paper provides a unified framework and a comprehensive survey of recent work in this rapidly growing field. Mqeac utilizes multiple quantum probability amplitude vectors to model the promising areas of solution space. Quantum inspired evolutionary algorithms qea are population based meta heuristics that. Improved quantuminspired evolutionary algorithm for. Genetic algorithms gas are a class of evolutionary algorithms inspired by darwinian natural selection. Contrary to genuine quantum algorithms, the considered algorithms do not require a useful quantum computer for its proficient execution. Quantuminspired evolutionary algorithm qea recently. The study assesses the performance of the qiea on a series of benchmark problems and compares the results with those from a canonical genetic algorithm.
Like other evolutionary algorithms, qea is also characterized by the representation of the individual, evaluation function, and population dynamics. Pdf quantuminspired genetic algorithms researchgate. Secondly, with a small number of individuals, even with one individual, qiea can. Quantum computing is an emerging interdisciplinary, combining the information science and quantum mechanics, and its integration with intelligent optimization algorithms begun in the 1990s. Pdf analysis of quantuminspired evolutionary algorithm. Quantuminspired evolutionary algorithm for numerical. Abstract quantum inspired evolutionary algorithm qiea is a probability based optimization algorithm which applies quantum computing principles such as qubits, superposition, quantum gate and quantum measurement to enhance the properties of classical evolutionary algorithms. Even though qea is based on the idea of quantum computing, it is not a quantum algorithm, but a classical evolutionary algorithm. Circuit testing 4, software testing 5, economic dispatch 6, 7. Quantuminspired evolutionary algorithms for calibration of the vg option.
The experimental results on the knapsack problemdemonstrated the effectiveness and the applicability of qea 5, 6. An improved quantuminspired evolutionary algorithm is proposed for solving mixed discretecontinuous nonlinear problems in engineering design. The proposed latin square quantuminspired evolutionary algorithm lsqea combines latin squares and quantumins. Quantuminspired 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 quantuminspired genetic algorithmbased optimization method for mobile impact test data integration 4 continuous rigidframe bridge are studied respectively to verify the effectiveness of the proposed method. Analysis of quantuminspired evolutionary algorithm 2001. Quantuminspired hybrid algorithm for integrated process. Well focus on the algorithm for solving linear systems of equations. Quantum inspired genetic algorithms was firstly introduced in.
The results from the algorithm are shown to be robust and comparable to those of other algorithms. This paper introduces an evolutionary algorithm which uses the concepts and principles of the quantuminspired evolutionary approach and the hierarchical arrangement of the compartments of a p system. An improved quantuminspired evolutionary algorithm for. An improved quantuminspired genetic algorithm for image. This project develops methods and software systems of quantum inspired evolutionary computation for the optimisation of parameters of intelligent systems. Solving combinatorial optimization problems with quantum. When addressing such problems, genetic algorithms typically have difficulty maintaining feasibility from parent to offspring. In this paper, a new multiobjective hwsw cosynthesis algorithm based on the quantuminspired evolutionary algorithm mqeac is proposed. A quantuminspired evolutionary algorithm based on p systems 279 diversity. This study illustrates how a quantuminspired evolutionary algorithm can be constructed and examines the utility of the resulting algorithm on a problem in. On the analysis of the quantuminspired evolutionary algorithm with a single individual kukhyun han, member, ieee, and jonghwan kim, senior member, ieee abstractthis paper discusses the reason why qea works and veri. On the analysis of the quantuminspired evolutionary.
Quantum algorithm an overview sciencedirect topics. Quantuminspired evolutionary algorithm for a class of combinatorial optimization abstract. A very similar description applies to the algorithm for recommendation systems. An efficient software implementation of quantum algorithms requires quantum computers capable of satisfying the deutschchurchturing. Pdf a novel evolutionary computing methodquantum inspired genetic. This section is mainly intended to outline the basic concepts of quantuminspired evolutionary algorithms. Performance comparison of populationbased quantuminspired. Quantum inspired genetic algorithm knowledge engineering and. An improved quantuminspired evolutionary algorithm iqea is presented in this paper to improve the clustering result of a data clustering problem. An adaptive quantumbased evolutionary algorithm for. Quantum inspired evolutionary algorithm for community. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i.
Quantuminspired genetic algorithm qga is a new optimization algorithm which combines the concept of quantum computing and classical ga. Paper contains selected results of theoretical and practical research concerning the possibility of creating evolutionary algorithms inspired by quantum information technology to improve the performance of neural models. Quantum inspired evolutionary algorithm to improve. Comparing the performance of quantuminspired evolutionary algorithms for the solution of software requirements selection problem. The first proposal of the algorithm drawing inspiration from both biological evolution and unitary evolution of quantum systems has been presented by narayanan and moore in 1996. Genetic algorithms and random keys for sequencing and. Quantuminspired evolutionary algorithm pseudocode it is easy to see that the pseudocode corresponds directly to the general classical evolutionary algorithm scheme. Evaluation, hybridization and application of quantum. I would not assume that there needs to be one universal answer, nor that these possibilities are only these two. Qeas integrate the principles of quantum computing and evolutionary algorithms to increase the search capabilities in terms of exploration and exploitation of the search space and to provide a platform. Since then quantuminspired evolutionary algorithms qeas are applied to solve optimization problems in various disciplines, including image processing, network design, flow shop scheduling, power system optimization, engineering optimization and training fuzzy neural networks. This paper proposes a novel evolutionary algorithm inspired by quantum computing, called a quantuminspired evolutionary algorithm qea, which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Quantuminspired hybrid algorithm for integrated process planning and scheduling mi liu, shuping yi, and peihan wen proceedings of the institution of mechanical engineers, part b.
In this paper a hwsw platform for the implementation of an optimizer based on a evolutionary algorithm, called quantuminspired evolutionary algorithm qea, is introduced. Research and applications explores the latest quantum computational intelligence approaches, initiatives, and applications in computing, engineering, science, and business. In, a genetic quantum algorithm was proposed by han et al. Qea is a population based algorithm which uses the concepts of quantum bits and superposition of. They were used for research on advanced randomised search algorithms mainly quantuminspired evolutionary and genetic algorithms and other population methods for numerical and combinatorial optimisation. It investigates the characteristics of qea which is based on the concept and principles of quantum computing such as quantum bit and linear superposition of states. This paper extends the authors previous works on quantuminspired evolutionary algorithm qea. This work presents a new model for the automatic synthesis of fuzzy classifiers, based on quantuminspired evolutionary algorithms, which overcomes the difficulties inherent to the use of hybrid representations and the treatment of multiple objectives, both necessary for the synthesis of these types of systems. Quantuminspired evolutionary algorithm for a class of. Quantuminspired evolutionary algorithms for financial.
Like the other qeabased algorithms, the iqea uses qbits to denote the state of a quantum particle and qgate as. A quantuminspired evolutionary algorithm is a new evolutionary algorithm for a classical computer rather than for quantum mechanical hardware. A quantuminspired evolutionary algorithm using gaussian. It is informally shown that the quantum inspired genetic algorithm performs. The quantum inspired evolutionary algorithms are placed at the crossing point of two subareas of software engineering, qc and evolutionary computing see fig. This repository contains some unpublished before source codes developed by robert nowotniak in the years 20102015. C srinivas, k comparing the performance of quantuminspired evolutionary algorithms for the solution of software requirements.
In 2002 han introduced a novel evolutionary algorithm inspired by quantum computing, growing from this date the number of publications on quantuminspired genetic algorithms. Quantum inspired computational intelligence 1st edition. However, the main stages of quantuminspired evolution ary algorithm are modelled upon concepts and principles of. To demonstrate its numerical performance, experiments on the knapsack problem have been carried out. Quantuminspired evolutionary algorithm for real and. Solving combinatorial optimization problems with quantum inspired. A novel evolutionary computing methodquantum inspired genetic algorithmsis introduced, where concepts and principles of quantum mechanics are used to inform and inspire more efficient. Citeseerx document details isaac councill, lee giles, pradeep teregowda. Both can be suboptimal, for many kinds of problems. Quantuminspired evolutionary algorithms for calibration.
Modern antiviral software systems avss are unable to identify new. Quantuminspired particle swarm optimization algorithm. On the analysis of the quantuminspired evolutionary algorithm with. In section 5, the objectives of the proposed research are outlined, followed by references. A quantuminspired evolutionary algorithm based on p. An algorithm can treat the stability between exploration and exploitation. Comparing the performance of quantuminspired evolutionary. The quantum monte carlo quantum annealing qmcqa 1 or discretetime simulated quantum annealing sqa 2 algorithms performed better than the tested dwave device in recent studies we establish the first example of a scaling advantage for an experimental quantum annealer over classical simulated annealing. The proposed model, called quantuminspired acromyrmex evolutionary algorithm qiaea, is inspired in the acromyrmex ant species, also known as leafcutter ants. Particular focus was on calculating the quantum available for use in quantum evolutionary algorithms.
International journal of soft computing and engineering. Quantuminspired acromyrmex evolutionary algorithm scientific. The proposed latin square quantuminspired evolutionary algorithm lsqea combines latin squares and quantuminspired genetic algorithm qga. In this paper we present a general genetic algorithm to address a wide variety of sequencing and optimization problems including multiple machine scheduling, resource allocation, and the quadratic assignment problem. Quantuminspired differential evolutionary algorithm for. Tianmin zheng and mitsuo yamashiro april 26th 2011. Evaluation, hybridization and application of quantum inspired evolutionary algorithms a brief outline of the proposed research to be carried out in pursuance for the award of the degree of doctor of philosophy in physics and computer science area of research evolutionary computation submitted by rajanampalle saran pavithr supervisor.
430 516 335 1148 1260 863 644 1291 240 466 32 4 785 1069 1415 342 1237 1346 977 634 1262 1301 623 1480 1379 1269 1135 1479 1380 669 362 73 1383 386 360 717 1196