Comparisons of several variants of continuous quantum-inspired evolutionary algorithms

Abstract

In this study, an extensive numerical analysis is carried out to investigate the effects of different quantum-based operators on the performance of continuous quantum-inspired evolutionary algorithms (QEAs). In this context, different variants of quantum-inspired evolutionary operators are adopted for numerical simulations. Furthermore, some novel chaos-enhanced QEAs are proposed and their performances are evaluated through the numerical comparative study. Based on evaluating the accuracy, robustness, convergence, scalability and sensitivity to initialisation of the rival methods, it is indicated that the algorithmic structure of QEAs is prone to being combined with chaotic maps. The results demonstrate that chaotically implemented QEAs can effectively explore/exploit the solution spaces of different landscapes and dimensionality, and finally, converge to acceptable regions within the solution domain.     

Design of metasurface polarization converter for near‐field application; stacked conical horn antenna for circular polarization

In this article, a three‐layer transmission mode linear to circular polarization converter (TMCP) is proposed with low‐level reflection coefficient and axial ratio (AR) at 9 GHz. Each layer of the proposed TMCP consists of microstrip patches and lines, providing inductive TM impedance and capacitive TE impedance on each layer to produce 90° out of phase between TE and TM electromagnetic waves. By removing the middle layer of the proposed metamaterial, a two‐layer TMCP with low‐level axial ratio at 8 GHz is obtained. However, the proposed two‐layer TMCP suffers from disagreements between the reflection coefficients of the TE and TM modes, which produce fluctuations on the AR results in the near‐field application. Low‐level reflection coefficient and AR at 9 GHz achieved from the three‐layer TMCP, indeed, make it efficient for the near‐field of a horn antenna. Unit cells of the two‐layer and three‐layer TMCPs consisting patches and lines are simulated using periodic boundary conditions. In addition, the patches and lines are printed on a FR4 substrate and cut out in circle shapes. The three substrates are arranged at the determined separations and then installed in front of a profiled conical horn antenna. The measured AR of the antenna stacked by the three‐layer TMCP is below 2 dB from 8.45 up to 9.6 GHz.     

Stability and Stabilization of Fractional‐Order Systems with Different Derivative Orders: An LMI Approach

Stability and stabilization analysis of fractional‐order linear time‐invariant (FO‐LTI) systems with different derivative orders is studied in this paper. First, by using an appropriate linear matrix function, a single‐order equivalent system for the given different‐order system is introduced by which a new stability condition is obtained that is easier to check in practice than the conditions known up to now. Then the stabilization problem of fractional‐order linear systems with different fractional orders via a dynamic output feedback controller with a predetermined order is investigated, utilizing the proposed stability criterion. The proposed stability and stabilization theorems are applicable to FO‐LTI systems with different fractional orders in one or both of 0 <  α  < 1 and 1 ≤  α  < 2 intervals. Finally, some numerical examples are presented to confirm the obtained analytical results.     

Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence

Multi objective processing can be leveraged for mining the association rules. This paper discusses the application of multi objective genetic algorithm to association rule mining. We focus our attention especially on association rule mining. This paper proposes a method based on genetic algorithm without taking the minimum support and confidence into account. In order to improve algorithm efficiency, we apply the FP-tree algorithm. Our method extracts the best rules that have best correlation between support and confidence. The operators of our method are flexible for changing the fitness. Unlike the Apriori-based algorithm, it does not depend on support. Experimental study shows that our technique outperforms the traditional methods.     

Intelligent multi-stakeholder environmental management

In any group decision making, stakeholders have different powers, proficiency and also experiences. These power weights are very difficult to obtain, because group managers avoid revealing the relative powers of the stakeholders to prevent more conflict among them. Therefore, in many studies, the different powers have not been well accounted and then equal power weights have been assigned to each stakeholder. This paper will show that considering the powers is necessary and then it introduces a new intelligent approach to obtain consensus based relative power weights. This method is based on the opinions of the stakeholders on the alternatives. A case study of watershed management is used to illustrate the application of the model to a real decision making problem. A suitable aggregation operator is also used to combine the goodness measures, considering the optimism/pessimism view of the group manager. Results indicate that obtaining the stakeholders’ weights and also considering the preferences of the group manager on the risk are essential parts for soft group decision making process, especially in the environmental management problems.     

A new real-coded stochastic Bayesian optimization algorithm for continuous global optimization

Estimation of distribution algorithms are considered to be a new class of evolutionary algorithms which are applied as an alternative to genetic algorithms. Such algorithms sample the new generation from a probabilistic model of promising solutions. The search space of the optimization problem is improved by such probabilistic models. In the Bayesian optimization algorithm (BOA), the set of promising solutions forms a Bayesian network and the new solutions are sampled from the built Bayesian network. This paper proposes a novel real-coded stochastic BOA for continuous global optimization by utilizing a stochastic Bayesian network. In the proposed algorithm, the new Bayesian network takes advantage of using a stochastic structure (that there is a probability distribution function for each edge in the network) and the new generation is sampled from the stochastic structure. In order to generate a new solution, some new structure, and therefore a new Bayesian network is sampled from the current stochastic structure and the new solution will be produced from the sampled Bayesian network. Due to the stochastic structure used in the sampling phase, each sample can be generated based on a different structure. Therefore the different dependency structures can be preserved. Before the new generation is generated, the stochastic network’s probability distributions are updated according to the fitness evaluation of the current generation. The proposed method is able to take advantage of using different dependency structures through the sampling phase just by using one stochastic structure. The experimental results reported in this paper show that the proposed algorithm increases the quality of the solutions on the general optimization benchmark problems.     

A quantum particle swarm optimization for the 0–1 generalized knapsack sharing problem

This study proposes a new hybrid heuristic approach that combines the quantum particle swarm optimization (QPSO) technique with a local search phase to solve the binary generalized knapsack sharing problem (GKSP). The approach also incorporates a heuristic repair operator that uses problem-specific knowledge instead of the penalty function technique commonly used for constrained problems. This study is the first to report on the application of the QPSO method to the GKSP. The efficiency of our proposed approach was tested on a large set of instances, and the results were compared to those produced by the commercial mixed integer programming solver CPLEX 12.5 of IBM-ILOG. The Experimental results demonstrated the good performance of the QPSO in solving the GKSP.     

A novel hybrid method for selecting soccer players during the transfer season

The quality of its players is one of the most significant features determining the failure or success of a sports team. The wide array of factors contributing to the performance of the players together with the inherent financial limitations of the clubs have transformed the selection of players into a complex problem. The current paper presents an integrated approach that combines multiple‐criteria decision‐making analysis and mathematical programming to support the decision maker through the building process of a soccer team. First, the fuzzy analytic network process is applied to evaluate the significance of the different performance criteria for each position in the field. The score attained by the different players in each potential position is computed using PROMETHEE II. A biobjective integer programming model has been designed to evaluate the transfer status of the players. Finally, data envelopment analysis is used to identify the most efficient Pareto solution determining the status of each player. In order to demonstrate the applicability of the proposed approach, the position in the field and transfer status of 60 players being considered by a real soccer team have been determined.     

Stability analysis in identification of interval type‐2 adaptive neuro‐fuzzy inference system: Contribution to a novel Lyapunov function

In recent years, control research has strongly highlighted the issue of training stability in the identification of non‐linear systems. This paper investigates the stability analysis of an interval type‐2 adaptive neuro‐fuzzy inference system (IT2ANFIS) as an identifier through a novel Lyapunov function. In so doing, stability analysis is initially conducted on the IT2ANFIS identifier, while performing the online training of both the antecedent and the consequent parameters by the gradient descent (GD) algorithm. In addition, the same stability analysis is carried out when the antecedent and the consequent parameters are trained by GD and forgetting factor recursive least square (FRLS) algorithms, respectively (GD + FRLS). A novel Lyapunov function is proposed in this study in order for the identifier stability to attain the required conditions. These conditions determine the permissible boundaries for the covariance matrix and the learning rates at every iteration of the identification procedure. Stability analysis reveals that wide range of learning rates is obtained. Furthermore, simulation results indicate that when the permissible boundaries are selected according to the proposed stability analysis, a stable identification process with appropriate performance is achieved.