Quantum-inspired Tabu Search Algorithm Research Articles

Intelligent Hybrid Trading Strategies Based on Quantum-Inspired Algorithm

Investing in stocks is a common choice for financial management. Technical indicators (TIs) assist investors in determining the best trading time to make a fortune. Moving average (MA) and relative strength index (RSI) are the most common TIs. The proposed hybrid technique maximizes the capabilities of these two indicators. This study utilizes the quantum-inspired algorithm to assist effectively in searching for the optimal solution in the vast solution space. The proposed trading system contains four innovative features. First, the traditional usage restriction of MA and RSI is removed to increase their potential effectiveness and identify the most profitable trading strategy. Second, this research proposes an innovative hybrid indicator (HI) that combines MA and RSI to simultaneously achieve both benefits. HI eliminates the restriction of employing a single indicator at once. Third, an efficient quantum-inspired algorithm, the Global-best-guided Quantum-inspired Tabu Search Algorithm with Quantum NOT Gate (GNQTS), effectively and efficiently explores optimized parameters. Fourth, this study proposes 60 sliding windows to determine the optimal period for training and testing. The investment targets include well-known indices: DJIA, S&P 500, NASDAQ Composite Index, and NYA, as well as high-reputation companies on the US stock market: DJIA components. By removing the restrictions imposed by these two indicators and the use of HI, the experiment results demonstrate that GNQTS can discover optimal parameters to generate higher returns than state-of-the-art methods and buy-and-hold (B&H) strategies. The proposed hybrid strategy provides for the promising prospect of quantum-inspired applications and the utilization of multiple indicators.

Joint Uplink Power Control, Downlink Beamforming, and Mode Selection for Secrecy Cell-Free Massive MIMO With Network-Assisted Full Duplexing

In this article, we investigate a secrecy cell-free massive multiple-input multiple-output system with network-assisted full duplexing for both noncolluding eavesdropper (Eve) and colluding Eve cases, where the access points (APs) are designed to serve downlink users and uplink users simultaneously and flexibly in the presence of Eves. To confirm the secrecy transmission, we consider the problem of jointly optimizing duplex mode selection and secrecy transceivers to maximize the overall secrecy spectral efficiency, where the information signals at APs are injected with artificial noise (AN) to prevent interception of information by Eves. Considering that downlink secure beamforming, uplink transmission power, and uplink receivers are tightly coupled in both the objective function and the constraints, we propose a two-loop strategy to solve the optimization problem. In particular, we propose a quantum-inspired tabu search algorithm to find a better duplex mode in the outer loop for each iteration. Then, for the inner loop, we propose a successive convex approximation based iterative algorithm to optimize the transceivers and AN by fixing the duplex mode selection vectors obtained from the outer loop. To address the proposed inner loop design subproblem, we resolve the subproblem by a series of nonconvex-convex approximate methods, such as the equivalent transformation and the iterative concave-convex procedure. The simulation results show that the proposed solution can achieve a higher secrecy spectral efficiency gain than the fixed-mode duplex scheme and simple greedy duplex mode selection schemes and achieve secrecy spectral efficiency performance similar to that of the optimal exhaustive search solution for both noncolluding Eve and colluding Eve cases.

Portfolio Optimization in Both Long and Short Selling Trading Using Trend Ratios and Quantum-Inspired Evolutionary Algorithms

Stock selection is the first problem that investors encounter when investing in the stock market and is paramount. The Sharpe ratio is a common assessment strategy. However, the Sharpe ratio considers an uptrend portfolio high risk because it assesses portfolio risk using standard deviations. Hence, we propose a novel investment strategy, namely, the trend ratio, to assess portfolio risk more accurately by the portfolio trend line. Thus, the uptrend portfolio is not considered high risk and is more consistent with the psychology of investors. In addition to normal trading (long selling), short selling is another common trading method. Short selling is borrowing stocks from stock vendors to sell and then repaying the stock at a lower price to make a profit. This paper proposes investing simultaneously in normal trading and short selling by a trend ratio, which can further increase investment profits and spread risks. This paper also adds certificates of deposit as a portfolio choice to ensure that investors can still make profits. This paper utilizes the global quantum-inspired tabu search algorithm with a quantum NOT-gate (GNQTS) to effectively find the best combination of stocks. To avoid the overfitting problem, this paper employs a sliding window. Specifically, this paper combines the trend ratio, GNQTS, short selling with certificates of deposit, and sliding windows to perform the stock selection. The experimental results are promising, with our proposed method having better performance than the Sharpe ratio. Furthermore, the experimental results show that both long selling and short selling investments can increase the performance.

Using Trend Ratio and GNQTS to Assess Portfolio Performance in the U.S. Stock Market

Stock selection is an important issue in the stock market, and when assessing portfolio performance, return and risk are important conditions. The Sharpe ratio is a well-known assessment strategy that simultaneously considers portfolio return and risk. However, as the Sharpe ratio uses the average line to assess portfolio risk, it can easily assess a portfolio with stable uptrend as high risk, thus, this paper uses the trend ratio to address this problem. The trend ratio can assess a stable uptrend portfolio with low risk and identify a portfolio that has a higher daily expected return per unit daily risk. As the solution space in stock solution is huge, it is hard to use brute-force method to exhaust selections within a limited time. Thus, this paper uses the Global-best guided Quantum-inspired Tabu Search algorithm with Not-gate (GNQTS) to effectively optimize a portfolio given limited time. In addition, this paper uses 13 different kinds of sliding windows to train and test data, and identify a suitable period with a high trend ratio. The experimental results show that the proposed method can identify a portfolio with stable uptrend in the U.S. stock market. This paper does not exclude any stocks in the solution space; therefore, our method can comprehensively consider all the possible combinations of a portfolio. Moreover, we find some interesting phenomenon: the best single stock may not be contained in a portfolio and the stock with negative return cannot be excluded in the solution space. In addition, this study compares our portfolio performance with the portfolio guided by the Sharpe ratio; according to the experimental results, a portfolio guided by the trend ratio has more stable uptrend and lower risk than the Sharpe ratio. Thus, our method can outperform other assessment strategies.

Building Intelligent Moving Average-Based Stock Trading System Using Metaheuristic Algorithms

Many studies have been proposed to prove that technical analysis can help investors make trading decisions. The moving average (MA) is a widely used technical indicator that plays an important role in this field since it directly reflects stock fluctuations. However, most studies ignore the parameter settings of the MA, which leads to underestimation of the potential of the MA. Therefore, this paper is the first attempt to remove all restrictions and extend the limits of the MA to take advantage of the MA’s capability well. It also uses different kinds of MA, such as the weighted moving average (WMA) and the exponential moving average (EMA), to compose trading strategies. Our system proposes the global best-guided quantum-inspired tabu search algorithm (GQTS), which is better at searching than traditional algorithms, to optimize trading strategies based on the MA. Furthermore, an innovative 2-phase sliding window is invented to consider more investment situations in changeable stock markets. In summary, this paper intended to investigate the ability of MA and proposed dynamic and intelligent trading strategies based on MA, GQTS, and 2-phase sliding window to assist investors to make trading decisions. The experiments show that the proposed system flexibly discovers better trading points. Our system outperforms traditional methods and beats the buy-and-hold strategy to yield significant profits both in developed and emerging stock markets.

A Novel Portfolio Optimization Model Based on Trend Ratio and Evolutionary Computation

Stock selection is a paramount importance in stock investment. Selecting stocks generally requires considering return and risk simultaneously. The common assessment strategy is to use the Sharpe ratio. Sharpe ratio uses the standard deviation to determine portfolio risk but a portfolio with an uptrend has high risk, defying the logic of most investors. Therefore, this paper proposes a novel assessment strategy, trend ratio. The trend ratio uses linear regression, which is improved with the initial funds when seeking to find the trend of a portfolio. The slope of the trend line is daily expected return, and the difference between the trend line and the funds standardization of a portfolio is daily risk. The trend ratio not only can find the portfolio that has a stable uptrend, but also solves the problem of the Sharpe ratio when a portfolio with an uptrend has high risk. This paper does not limit the number of stocks in a portfolio; it uses the quantum-inspired tabu search algorithm, which is improved by an adaptive strategy, the current best-known solution, and the quantum not gate (ANQTS) to find the best portfolio in a large solution space. This paper employs the sliding window to avoid the over-fitting problem. We use the traditional sliding window, which uses the previous period as training data, as well as the year-on-year sliding window, which uses the same period in the last year as the training data. In summary, this paper combines the trend ratio, ANQTS, and the sliding window to solve the problem of stock selection. The experimental results show that our method finds better portfolios than the Sharpe ratio. Moreover, the year-on-year sliding window, as proposed herein, has promising performance in investment and finds out some suitable economic cycle lengths for stock investment.

Quantum entropy based tabu search algorithm for energy saving in SDWN

The energy consumption of the base station (BS) accounts for great proportion of the total wireless access network (WAN). Switching off the selected spare BSs with few network request would save a large amount of energy. It is difficult to deploy a BS energy saving strategy in existing network architecture due to the tightly coupled network devices. Therefore, we adopt the software defined wireless networks (SDWN) structure which is an sample of the wireless software defined networks (SDN). Then a novel quantum entropy based tabu search algorithm (QETS) is proposed to choose which BS to switch off, and it increases the search range and guarantee the convergence speed. The energy saving strategy can find the optimal solution with higher probabilities and can be deployed in centralized controller as a software. Theoretical analysis and simulation results show the QETS algorithm’s gain over the greedy algorithm and quantum inspired tabu search algorithm (QTS) in terms of convergence.

Entanglement-Enhanced Quantum-Inspired Tabu Search Algorithm for Function Optimization

Many metaheuristic algorithms have been proposed to solve combinatorial and numerical optimization problems. Most optimization problems have high dependence, meaning that variables are strongly dependent on one another. If a method were to attempt to optimize each variable independently, its performance would suffer significantly. When traditional optimization techniques are applied to high-dependence problems, they experience difficulty in finding the global optimum. To address this problem, this paper proposes a novel metaheuristic algorithm, the entanglement-enhanced quantum-inspired tabu search algorithm (Entanglement-QTS), which is based on the quantum-inspired tabu search (QTS) algorithm and the feature of quantum entanglement. Entanglement-QTS differs from other quantum-inspired evolutionary algorithms in that its $Q$ -bits have entangled states, which can express a high degree of correlation, rendering the variables more intertwined. Entangled Q-bits represent a state-of-the-art idea that can significantly improve the treatment of multimodal and high-dependence problems. Entanglement-QTS can discover optimal solutions, balance diversification and intensification, escape numerous local optimal solutions by using the quantum not gate, reinforce the intensification effect by local search and entanglement local search, and manage strong-dependence problems and accelerate the optimization process by using entangled states. This paper uses nine benchmark functions to test the search ability of the entanglement-QTS algorithm. The results demonstrate that Entanglement-QTS outperforms QTS and other metaheuristic algorithms in both its effectiveness at finding the global optimum and its computational efficiency.

Quantum-inspired algorithm for cyber-physical visual surveillance deployment systems

With recent advances in camera and visual computing technology, visual surveillance is playing a significant role in wireless sensor networks (WSNs) and cyber-physical systems (CPSs). It can be used in civilian areas for traffic control and security monitoring. Deployment is an important and fundamental issue in a WSN/CPS. Many issues, such as the quality of service, energy efficiency, and lifetime, are based on the placement of sensors. Different heuristic and deterministic methods have been proposed to achieve optimal deployment. In this paper, we propose a novel algorithm, called the Quantum-inspired Tabu Search algorithm with Entanglement (QTSwE), which is based on both the Quantum-inspired Tabu Search (QTS) algorithm and quantum entanglement feature. QTSwE is applied to a deployment problem to determine the minimum number of sensors required and their locations. This paper analyzes the property of the deployment problem and calls this phenomenon dependency. It uses the concept of quantum entanglement to build the initial positions of sensors to tackle the dependency of variables. The QTS is then used to find better solutions iteratively. Moreover, we use local search to enhance the search capability of QTS and to avoid being trapped in local optima. The experiment results showed that QTSwE outperformed other deployment approaches and used the least number of sensors to satisfy the monitoring requirement and topology connectivity. With QTSwE, the performance of surveillance system deployment has improved further.

A Rule-Based Dynamic Decision-Making Stock Trading System Based on Quantum-Inspired Tabu Search Algorithm

Heuristic methods or evolutionary algorithms (such as genetic algorithms and genetic programs) are common approaches applied in financial applications, such as trading systems. Determining the best time to buy or sell stocks in a stock market, and thereby maximizing profit with low risks, is an important issue in financial research. Recent studies have used trading rules based on technique analysis to address this problem. This method can determine trading times by analyzing the value of technical indicators. In other words, we can make trading rules by finding the trading value of technique indicators. An example of a trading rule would be, if one technical indicator's value achieves the setting value, then either buy or sell. A combination of trading rules would become a trading strategy. The process of making trading strategies can be formulated as a combinational optimization problem. In this paper, we propose a novel method for applying a trading system. First, the proposed method uses the quantum-inspired Tabu search algorithm to find the optimal composition and combination of trading strategies. Second, this method uses a sliding window to avoid the major problem of over-fitting. The experiment results of earning money show much better performance than other approaches, and the proposed method outperforms the buy and hold method (which is a benchmark in this field).

A quantum-inspired Tabu search algorithm for solving combinatorial optimization problems

In this study, we propose a novel quantum-inspired evolutionary algorithm (QEA), called quantum inspired Tabu search (QTS). QTS is based on the classical Tabu search and characteristics of quantum computation, such as superposition. The process of qubit measurement is a probability operation that increases diversification; a quantum rotation gate used to searching toward attractive regions will increase intensification. This paper will show how to implement QTS into NP-complete problems such as 0/1 knapsack problems, multiple knapsack problems and the traveling salesman problem. These problems are important to computer science, cryptography and network security. Furthermore, our experimental results on 0/1 knapsack problems are compared with those of other heuristic algorithms, such as a conventional genetic algorithm, a Tabu search algorithm and the original QEA. The final outcomes show that QTS performs much better than other heuristic algorithms without premature convergence and with more efficiency. Also on multiple knapsack problems and the traveling salesman problem QTS verify its effectiveness.