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).
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 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.
In this paper, we propose a novel quantum-inspired evolutionary algorithm, called NQEA, for solving combinatorial optimization problems. NQEA uses a new Q-bit update operator to increase the balance between the exploration and exploitation of the search space. In the operator, first, the Q-bits of each individual in the population are updated based on the personal best measurement of that individual and the best measurement of current generation. Then, a restriction is applied to each Q-bit to prevent the premature convergence of its values. The results of experiments on the 0-1 knapsack and NK-landscapes problems show that NQEA performs better than a classical genetic algorithm, CGA, and two quantum-inspired evolutionary algorithms, QEA and vQEA, in terms of convergence speed and accuracy.
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, 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.
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.
Quantum-inspired evolutionary algorithms (QIEAs), as a subset of evolutionary computation, are based on the principles of quantum computing such as quantum bits and quantum superposition. In this paper, we propose a multi-start quantum-inspired evolutionary algorithm, called MSQIEA. To improve the performance of the algorithm, a multi-measurement operator and a new strategy for updating the rotation angle is proposed. When Q-bit individuals start to converge to their final states, the best solution is stored and all Q-bits in each Q-bit individual are reinitialized. We compare the effectiveness of MSQIEA with a popular quantum-inspired evolutionary algorithm, called QEA, for solving 0-1 knapsack problem. The experimental results show that MSQIEA outperforms QEA and finds a solution with higher profit.