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International Conference on Next Generation Computing Technologies

Quantum evolutionary computational technique for constrained engineering optimization

V. Astuti, K. Hans Raj

Published Oct 1, 2016• 1 citation
Publisher

Abstract

A Quantum Evolutionary Computational Technique (QECT) is proposed in this paper. The approach is based on the integration of quantum computing concepts such as superposition of states, application of quantum gate with the concept of genetic algorithm and simulated annealing. To demonstrate effectiveness and applicability of QECT, simulations are carried out on five Benchmark Test functions, which are well-known combinatorial optimization problems. These exemplify that the proposed algorithm has a capability to obtain near global optimum, without premature convergence unlike other variants. QECT has the strong capability to explore the nonlinear search regions and it is a step forward in the area of hybrid stochastic search. It is applied on a problem of mechanical engineering design of a pressure vessel. The application of proposed heuristic technique in mechanical engineering design is a step towards agility in design and manufacturing.

DOI
10.1109/ngct.2016.7877430

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This paper proposes a novel evolutionary algorithm inspired by quantum computing, called a quantum-inspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, evaluation function, and population dynamics. However, instead of binary, numeric, or symbolic representation, QEA uses a Q-bit, defined as the smallest unit of information, for the probabilistic representation and a Q-bit individual as a string of Q-bits. A Q-gate is introduced as a variation operator to drive the individuals toward better solutions. To demonstrate its effectiveness and applicability, experiments were carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that QEA performs well, even with a small population, without premature convergence as compared to the conventional genetic algorithm.
Real world problems in engineering domain are typically constraint optimization problems.An Adaptive Quantum Evolutionary Algorithm for solving such problems is proposed in this paper.The proposed technique uses a novel qubits representation for search and optimization and uses feasibility rules for handling constraints.Moreover, it does not need stochastic ranking or niching or other methods for maintaining diversity.It does not even require mutation and local heuristics.The algorithm is tested on a standard set of four widely studied benchmark engineering design optimization problems.The results obtained are better than the existing state of the art approaches.The proposed algorithm is simple in concept and implementation, while being robust.
Quantum-inspired algorithms (QIAs) are computationally efficient algorithms that generate a quantum model in classical hardware as an alternative to the use of quantum models. In this research paper, the researchers carried out an experimental appraisal of four quantum-inspired algorithms in quantum-inspired Evolutionary Algorithm (QEA), quantum-behaved Particle Swarm Optimization (QPSO), quantum-inspired Genetic Algorithm (QIGA) and quantum Simulated Annealing (QSA) on benchmark optimization functions. The introduction of a simulation-based methodology was done in Python to test the convergence speed, global search capability, accuracy and robustness. According to the experimental findings, QPSO always performs well compared with other methods in the achievement of faster convergence and enhanced global optimality, but QEA is highly successful in cases of discrete combinatorial problems. The paper concludes that quantum-inspired models are effective in modeling the key quantum concepts like probabilistic superposition and tunneling to attain high quality optimization results with conventional computing platforms. Such results demonstrate the opportunities of QIAs as scalable pre-quantum tools of designing, AI, and industrial optimization.
Optimization is one of the most primary jobs of all the engineering problems. We are fortunate to have blessed with scores of methods available for optimization purpose. Evolutionary algorithm is fast becoming one of the most sought after methods for this purpose. This method depends upon random moves. So sometimes the time consumed by this algorithm is much more than expected. With an aim to get rid of this problem, in this paper we propose an entirely new approach of evolutionary algorithm based on quantum computing. A quantum evolutionary algorithm can be termed as a concept which utilizes certain tools of quantum computing for search and optimization purposes. A quantum algorithm relies heavily on qubits and quantum gates. Qubits are the entities which actually contain information in quantum computing. They can either remain in a secluded state or a superposition of two or more qubits states. When they are in a superposition state we employ the concepts of probability to predict where they can actually be found. On the other hand quantum gates utilize qubits to achieve some predefined goals. These predefined goals in case of an optimization problem are to maximize or minimize some sort of objective function. This paper proposes a novel evolutionary algorithm employing quantum computing to address optimization problems. We have tested our algorithm on a famous problem known as Gear Train Design Problem. The results so obtained outperform the classical optimization methods.
A novel evolutionary algorithm based on quantum computing concepts is applied to engineering optimization problems. Quantum computing mimics behavior of atoms in processing information. Quantum inspired algorithm is a concept, which employs certain elements of quantum computing to use in a wider class of search and optimization problems. The main parts of a quantum-inspired algorithm are the qubits (quantum equivalent of bits) and the quantum gates. Qubits hold the information in a superposition of all the states, while the quantum gates evolve the qubit to achieve the desired objective, which is, in optimization the maximum or the minimum. The paper addresses the ability of the Quantum-Inspired Evolution Algorithm (QIEA) to solve practical engineering optimization problems. QIEA, which is developed by authors, is based on their previous work and it is extended to acquire the floating-point representation of the information. The RQIEA is tested on a four benchmark engineering optimization problems. The results showed that the new algorithm has the ability to compete and even overcome other well known methods.