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International Journal of Computer Applications

An Adaptive Quantum Evolutionary Algorithm for Engineering Optimization Problems

Ashish Mani, C. Patvardhan

Published Feb 25, 2010• 11 citations
Publisher

Abstract

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.

DOI
10.5120/444-677

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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.
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.
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.
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.
This paper investigates the potential of incorporating quantum mechanical concepts into evolutionary algorithms (EAs) to improve their optimization capabilities. Traditional EAs, while effective, often struggle with complex search spaces and can get trapped in local optima. We propose a framework for developing quantum-inspired EAs that leverages principles such as superposition and entanglement to enhance exploration and exploitation strategies. The core idea is to introduce probabilistic operators and adaptive parameter control mimicking quantum behaviors. We present a detailed description of the proposed algorithm, focusing on the mathematical formulation of the key components, including the representation of solutions, the selection process, and the mutation operator. The algorithm's performance is evaluated on several benchmark optimization problems, demonstrating its potential to outperform conventional EAs in scenarios demanding high accuracy and rapid convergence. Furthermore, we analyze the theoretical implications of this approach, highlighting the advantages derived from quantum-inspired mechanisms. This work contributes to the ongoing research at the intersection of evolutionary computation and quantum information theory, offering a novel approach to tackling complex optimization challenges.
This paper investigates the application of quantum-inspired techniques to enhance the performance of evolutionary algorithms (EAs) for solving complex optimization problems. Traditional EAs often struggle with slow convergence rates, particularly in high-dimensional search spaces. We propose a novel approach that integrates concepts from quantum mechanics, specifically superposition and interference, into the selection and mutation operators of EAs. The core idea is to leverage the ability of quantum systems to explore multiple possibilities simultaneously, thereby accelerating the convergence process. We demonstrate the effectiveness of this hybrid algorithm through theoretical analysis and illustrate its potential through detailed mathematical formulations. The results indicate a significant improvement in convergence speed and robustness compared to standard EAs, suggesting a promising direction for future research in optimization. The primary focus is on developing an EA that mimics quantum superposition to examine multiple solutions simultaneously and then uses interference to guide the search towards optimal solutions. This approach aims to address the limitations of conventional EAs, particularly in challenging optimization scenarios.
Abstract The Quantum-Based Optimisation Method (QBOM) is a novel optimization approach based on quantum computing concepts. The novel optimization method's durability is studied using its capacity to conjoin with existing optimization techniques. This study uses The QBOM with the Pattern Search (PS) technique to solve engineering optimization problems. The first strategy, Hybrid I, uses QBOM for global search optimization, followed by PS searching in the nearby region for the optimum solution. The second strategy, Hybrid II, uses QBOM as a local search optimization within Pattern Search. In each iteration, QBOM starts searching inside PS for a better solution than the one detected at that stage, which is labelled as PS's new search point. These two hybrid techniques attempt to expand the possibilities of QBOM's local search mechanism while demonstrating its resilience. The hybridised methodologies are used to solve benchmark optimization problems and six real-world engineering optimization problems. The study revealed that the two hybrid techniques worked brilliantly, producing solutions that exceeded previous methods described in the literature for certain benchmark optimization problems. Not only did the hybridised methods produce better results in less computational time, but they also demonstrated that QBOM could be used to improve the search mechanism and accelerate the performance of the evolutionary algorithm in the local search to match its execution in the global search.