Model Construction of Enterprise Financial Early Warning Based on Quantum FOA-SVR

The sudden outbreak of COVID-19 has a great impact on human life security and global economic development. To deal with the rampant pandemic, many countries have taken strict control measures, including restricting gathering in public places and stopping the production of enterprises; as a result, many enterprises suffered great challenges in survival and development during the pandemic. In order to help enterprises monitor their own financial situation and realize their healthy development under the pandemic, this paper constructs an Enterprise Financial Early Warning Model, in which Quantum Rotation Gate is used to optimize four algorithms, namely, Fruit Fly Optimization Algorithm (QFOA), Bee Colony Optimization Algorithm (QABC), Particle Swarm Optimization (QPSO), and Ant Colony Optimization (QACO). The results show that the ability of the prediction model can be greatly improved by using the Quantum Rotation Gate to optimize these four algorithms.

Diminution of factual power loss by enhanced bacterial foraging optimization algorithm

<div data-canvas-width="126.37004132231402">This paper presents an enhanced bacterial foraging optimization (EBFO) algorithm for solving the optimal reactive power problem. Bacterial foraging optimization is based on foraging behaviour of <em>Escherichia coli</em> bacteria which present in the human intestine. Bacteria have inclination to congregate the nutrient-rich areas by an action called as Chemo taxis. The bacterial foraging process consists of four chronological methods i.e. chemo taxis, swarming and reproduction and elimination-dispersal. In this work rotation angle adaptively and incessantly modernized, which augment the diversity of the population and progress the global search capability. The quantum rotation gate is utilized for chemo taxis to modernize the state of chromosome projected EBFO algorithm has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively.</div>

An Improved Quantum Ant Colony Algorithm of Solving Nonlinear Equation Groups

In this paper, according to the characteristics of nonlinear equations. A new algorithm, Quantum Ant Colony Algorithm (QACA) is proposed. The core is that Q-bit and quantum rotation gate adopted in QEA are introduced into ACS to represent and update the pheromone respectively, so it has better diversity and global search capacity. The experimental result demonstrates that QACA can get better solutions to nonlinear equations.

A DE-Based Hybrid Algorithm for the Flexible Job-Shop Scheduling Problem

This paper presents a hybrid algorithm to address the flexible job-shop scheduling problem (FJSP). Based on Differential Evolution (DE), a global search method is introduced in the hybrid algorithm, where variations are made to the mutation and crossover operators in DE, according to the quantum rotation gate. And an Interchange-based local search method is further adopted in the proposed algorithm to gain a better performance. Experiments are performed to show the efficiency of the proposed algorithm.