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ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

Advanced Quantum Inspired Evolutionary Algorithm for Multivariate Optimization

Sai Siddhartha Vivek Dhir Rangoju, Om Prakash Patel, Neha Bharill

Published Dec 7, 2022• 1 citation
Publisher

Abstract

In real life, there are many applications where we need to take care of multiple parameters to get the optimized result. Similarly, many scientific and engineering problems require optimization of various parameters to get desired results. Many algorithms work well with a few variables to get optimized results, but on increasing the number of variables, they do not perform well. In this paper, we proposed an advanced quantum-inspired evolutionary algorithm (A-QEAM) to solve optimization problems where the tuning of multiple parameters or variables is required. A-QEAM is characterized by the principle of quantum computing such as superposition and qubit. This algorithm uses a qubit in place of the classical bit. The proposed algorithm is tested on mathematical functions consisting of 2 variables, 10 variables, 30 variables, and 50 variables. The result shows that the proposed algorithm performs well even on increasing the number of variables.

DOI
10.1109/snpd54884.2022.10051777

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In real life, there are many applications where we need to take care of multiple parameters to get the optimized result. Similarly, many scientific and engineering problems require optimization of various parameters to get desired results. Many algorithms work well with a few variables to get optimized results, but on increasing the number of variables, they do not perform well. In this paper, we proposed an advanced quantum-inspired evolutionary algorithm (A-QEAM) to solve optimization problems where the tuning of multiple parameters or variables is required. A-QEAM is characterized by the principle of quantum computing such as superposition and qubit. This algorithm uses a qubit in place of the classical bit. The proposed algorithm is tested on mathematical functions consisting of 2 variables, 10 variables, 30 variables, and 50 variables. The result shows that the proposed algorithm performs well even on increasing the number of variables.
In this paper, an advanced quantum-inspired evolutionary optimization algorithm (A-QEOA) is proposed. This algorithm is based on the principle of quantum computing such as quantum bit representation and superposition of states. Like other evolutionary algorithms, the quantum-inspired evolutionary algorithm has characteristics of exploration and exploitation. The quantum-inspired evolutionary algorithm achieves exploration using observation process and exploitation using quantum rotation gate. Exploration helps to find the search space for a parameter that needs to be optimized. However, the observation process needs a random number which may sometimes increase the number of iterations to converge the algorithm or may lead to premature convergence. In this paper, we propose an algorithm with an updated observation process that helps to converge the algorithm optimally and helps to get results faster in comparison to the existing quantum inspired evolutionary algorithm (QEA).
This paper proposes a novel type of quantum-inspired evolutionary algorithm (QiEA) for numerical optimization inspired by the multiple universes principle of quantum computing, which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Numerical optimization problems are an important field of research with several applications in several areas: industrial plant optimization, data mining and many others, and although being successfully used for solving several optimization problems, evolutionary algorithms still present issues that can reduce their performances when faced with task where the evaluation function is computationally intensive. In order to address those issues the QiEA represent the most recent advance in the field of evolutionary computation. This work present some application about combinatorial and numerical optimization problems.
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.
In this paper, some guidelines for setting the parameters of quantum-inspired evolutionary algorithm (QEA) are presented. QEA is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. However, QEA is not a quantum algorithm, but a novel evolutionary algorithm. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, the evaluation function, and the population dynamics. From recent research on the knapsack problem, the results of QEA are better than those of CGA (conventional GA). Although the performance of QEA is excellent, there is relatively little or no research on the effects of different settings for its parameters. This paper describes some guidelines for setting these parameters. The guidelines are drawn up based on extensive experiments carried out for a class of combinatorial and numerical optimization problems. Through the guidelines, the performance of QEA can be maximized.
Quantum inspired Evolutionary Algorithms were proposed more than a decade ago and have been employed for solving a wide range of difficult search and optimization problems. A number of changes have been proposed to improve performance of canonical QEA. However, canonical QEA is one of the few evolutionary algorithms, which uses a search operator with relatively large number of parameters. It is well known that performance of evolutionary algorithms is dependent on specific value of parameters for a given problem. The advantage of having large number of parameters in an operator is that the search process can be made more powerful even with a single operator without requiring a combination of other operators for exploration and exploitation. However, the tuning of operators with large number of parameters is complex and computationally expensive. This paper proposes a novel heuristic method for tuning parameters of canonical QEA. The tuned QEA outperforms canonical QEA on a class of discrete combinatorial optimization problems which, validates the design of the proposed parameter tuning framework. The proposed framework can be used for tuning other algorithms with both large and small number of tunable parameters.
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.