Improved genetic algorithm for nonlinear programming problems

An improved genetic algorithm(IGA) based on a novel selection strategy to handle nonlinear programming problems is proposed.Each individual in selection process is represented as a three-dimensional feature vector which is composed of objective function value,the degree of constraints violations and the number of constraints violations.It is easy to distinguish excellent individuals from general individuals by using an individuals’ feature vector.Additionally,a local search(LS) process is incorporated into selection operation so as to find feasible solutions located in the neighboring areas of some infeasible solutions.The combination of IGA and LS should offer the advantage of both the quality of solutions and diversity of solutions.Experimental results over a set of benchmark problems demonstrate that IGA has better performance than other algorithms.     

Adaptive monopulse beamforming with partial parallel structure

A new recursive algorithm with the partial parallel structure based on the linearly constrained minimum variance (LCMV) criterion for adaptive monopulse systems is proposed. The weight vector associated with the original whole antenna array is decomposed into several adaptive weight sub-vectors firstly. An adaptive algorithm based on the conventional LCMV principle is then deduced to update the weight sub-vectors for sum and difference beam, respectively. The optimal weight vector can be obtained after convergence. The required computational complexity is evaluated for the proposed technique, which is on the order of O(N) and less than that of the conventional LCMV method. The flow chart scheme with the partial parallel structure of the proposed algorithm is introduced. This scheme is easy to be implemented on a distributed computer/digital signal processor (DSP) system to solve the problems of the heavy computational burden and vast data transmission of the large-scale adaptive monopulse array. Then, the monopulse ratio and convergence rate of the proposed algorithm are evaluated by numerical simulations. Compared with some recent adaptive monopulse estimation methods, a better performance on computational complexity and monopulse ratio can be achieved with the proposed adaptive method.     

Genetic algorithm for pareto optimum-based route selection

A quality of service (QoS) or constraint-based routing selection needs to find a path subject to multiple constraints through a network. The problem of finding such a path is known as the multi-constrained path(MCP) problem, and has been proven to be NP-complete that cannot be exactly solved in a polynomial time. The NPC problem is converted into a multiobjective optimization problem with constraints to be solved with a genetic algorithm. Based on the Pareto optimum, a constrained routing computation method is proposed to generate a set of nondominated optimal routes with the genetic algorithm mechanism. The convergence and time complexity of the novel algorithm is analyzed. Experimental results show that multiobjective evolution is highly responsive and competent for the Pareto optimum-based route selection. When this method is applied to a MPLS and metropolitan-area network, it will be capable of optimizing the transmission performance.     

Novel multi-objective optimization algorithm

Many multi-objective evolutionary algorithms （MOEAs） can converge to the Pareto optimal front and work well on two or three objectives, but they deteriorate when faced with manyobjective problems. Indicator-based MOEAs, which adopt various indicators to evaluate the fitness values （instead of the Paretodominance relation to select candidate solutions）, have been regarded as promising schemes that yield more satisfactory results than well-known algorithms, such as non-dominated sort- ing genetic algorithm （NSGA-II） and strength Pareto evolutionary algorithm （SPEA2）. However, they can suffer from having a slow convergence speed. This paper proposes a new indicatorbased multi-objective optimization algorithm, namely, the multi- objective shuffled frog leaping algorithm based on the ε indicator （ε-MOSFLA）. This algorithm adopts a memetic meta-heuristic, namely, the SFLA, which is characterized by the powerful capability of global search and quick convergence as an evolutionary strategy and a simple and effective E-indicator as a fitness assignment scheme to conduct the search procedure. Experimental results, in comparison with other representative indicator-based MOEAs and traditional Pareto-based MOEAs on several standard test problems with up to 50 objectives, show that ε-MOSFLA is the best algorithm for solving many-objective optimization problems in terms of the solution quality as well as the speed of convergence.     

A ε-indicator-based shuffled frog leaping algorithm for many-objective optimization problems

Many-objective optimization problems take challenges to multi-objective evolutionary algorithms.A number of nondominated solutions in population cause a difficult selection towards the Pareto front.To tackle this issue,a series of indicatorbased multi-objective evolutionary algorithms(MOEAs)have been proposed to guide the evolution progress and shown promising performance.This paper proposes an indicator-based manyobjective evolutionary algorithm calledε-indicator-based shuffled frog leaping algorithm(ε-MaOSFLA),which adopts the shuffled frog leaping algorithm as an evolutionary strategy and a simple and effectiveε-indicator as a fitness assignment scheme to press the population towards the Pareto front.Compared with four stateof-the-art MOEAs on several standard test problems with up to 50 objectives,the experimental results show thatε-MaOSFLA outperforms the competitors.     

Enhanced minimum attribute reduction based on quantum-inspired shuffled frog leaping algorithm

Attribute reduction in the rough set theory is an important feature selection method,but finding a minimum attribute reduction has been proven to be a non-deterministic polynomial(NP)-hard problem.Therefore,it is necessary to investigate some fast and effective approximate algorithms.A novel and enhanced quantum-inspired shuffled frog leaping based minimum attribute reduction algorithm(QSFLAR) is proposed.Evolutionary frogs are represented by multi-state quantum bits,and both quantum rotation gate and quantum mutation operators are used to exploit the mechanisms of frog population diversity and convergence to the global optimum.The decomposed attribute subsets are co-evolved by the elitist frogs with a quantum-inspired shuffled frog leaping algorithm.The experimental results validate the better feasibility and effectiveness of QSFLAR,comparing with some representative algorithms.Therefore,QSFLAR can be considered as a more competitive algorithm on the efficiency and accuracy for minimum attribute reduction.     

Application of quantum neural networks in localization of acoustic emission

Due to defects of time-difference of arrival localization,which influences by speed differences of various model waveforms and waveform distortion in transmitting process,a neural network technique is introduced to calculate localization of the acoustic emission source.However,in back propagation(BP) neural network,the BP algorithm is a stochastic gradient algorithm virtually,the network may get into local minimum and the result of network training is dissatisfactory.It is a kind of genetic algorithms with the form of quantum chromosomes,the random observation which simulates the quantum collapse can bring diverse individuals,and the evolutionary operators characterized by a quantum mechanism are introduced to speed up convergence and avoid prematurity.Simulation results show that the modeling of neural network based on quantum genetic algorithm has fast convergent and higher localization accuracy,so it has a good application prospect and is worth researching further more.     

Cooperative extended rough attribute reduction algorithm based on improved PSO

Particle swarm optimization (PSO) is a new heuristic algorithm which has been applied to many optimization problems successfully. Attribute reduction is a key studying point of the rough set theory, and it has been proven that computing minimal reduction of decision tables is a non-derterministic polynomial (NP)-hard problem. A new cooperative extended attribute reduction algorithm named Co-PSAR based on improved PSO is proposed, in which the cooperative evolutionary strategy with suitable fitness functions is involved to learn a good hypothesis for accelerating the optimization of searching minimal attribute reduction. Experiments on Benchmark functions and University of California, Irvine (UCI) data sets, compared with other algorithms, verify the superiority of the Co-PSAR algorithm in terms of the convergence speed, efficiency and accuracy for the attribute reduction.     

Support vector classification algorithm based on variable parameter linear programming

To solve the problems of SVM in dealing with large sample size and asymmetric distributed samples, a support vector classification algorithm based on variable parameter linear programming is proposed. In the proposed algorithm, linear programming is employed to solve the optimization problem of classification to decrease the computation time and to reduce its complexity when compared with the original model. The adjusted punishment parameter greatly reduced the classification error resulting from asymmetric distributed samples and the detailed procedure of the proposed algorithm is given. An experiment is conducted to verify whether the proposed algorithm is suitable for asymmetric distributed samples.     

BNE-based concurrent transmission considering channel quality and its PSO searching strategy in Ad Hoc networks

The paper proposes a decentralized concurrent transmission strategy in shared channels based on an incomplete information game in Ad Hoc networks.Based on the nodal channel quality,the game can work out a channel gain threshold,which decides the candidates for taking part in the concurrent transmission.The utility formula is made for maximizing the overall throughput based on channel quality variation.For an achievable Bayesian Nash equilibrium(BNE) solution,this paper further prices the selfish players in utility functions for attempting to improve the channel gain one-sidedly.Accordingly,this game allows each node to distributedly decide whether to transmit concurrently with others depending on the Nash equilibrium(NE).Besides,to make the proposed game practical,this paper next presents an efficient particle swarm optimization(PSO) model to fasten the otherwise very slow convergence procedure due to the large computational complexity.Numerical results show the proposed approach is feasible to increase concurrent transmission opportunities for active nodes and the convergence can be swiftly obtained with a few of iteration times by the proposed PSO algorithm.     

Constrained voting extreme learning machine and its application

Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.With the increase of the nodes in the hidden layers,the computation cost is greatly increased.In this paper,we propose a novel algorithm,named constrained voting extreme learning machine(CV-ELM).Compared with the traditional ELM,the CV-ELM determines the input weight and bias based on the differences of between-class samples.At the same time,to improve the accuracy of the proposed method,the voting selection is introduced.The proposed method is evaluated on public benchmark datasets.The experimental results show that the proposed algorithm is superior to the original ELM algorithm.Further,we apply the CV-ELM to the classification of superheat degree(SD)state in the aluminum electrolysis industry,and the recognition accuracy rate reaches87.4%,and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.     

Weapon target assignment problem satisfying expected damage probabilities based on ant colony algorithm

A weapon target assignment （WTA） model satisfying expected damage probabilities with an ant colony algorithm is proposed. In order to save armament resource and attack the targets effectively, the strategy of the weapon assignment is that the target with greater threat degree has higher priority to be intercepted. The effect of this WTA model is not maximizing the damage probability but satisfying the whole assignment result. Ant colony algorithm has been successfully used in many fields, especially in combination optimization. The ant colony algorithm for this WTA problem is described by analyzing path selection, pheromone update, and tabu table update. The effectiveness of the model and the algorithm is demonstrated with an example.     

Revised barrier function-based adaptive finite-and fixed-time convergence super-twisting control

This paper presents an adaptive gain, finite-and fixedtime convergence super-twisting-like algorithm based on a revised barrier function, which is robust to perturbations with unknown bounds. It is shown that this algorithm can ensure a finite-and fixed-time convergence of the sliding variable to the equilibrium, no matter what the initial conditions of the system states are, and maintain it there in a predefined vicinity of the origin without violation. Also, the proposed method avoids the prob...     

Optimization of Additively Decomposed Function with Constraints

We propose a modified evolutionary computation method to solve the optimization problem of additively decomposed function with constraints, ft is based on factorized distribution instead of penalty function and any transformation to a linear model or others. The feasibility and convergence of the new algorithm are given. The numerical results show that the new algorithm gives a satisfactory performance.     

Adaptive multi-feature tracking in particle swarm optimization based particle filter framework

This paper proposes a particle swarm optimization(PSO) based particle filter(PF) tracking framework,the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state transition stage,and simultaneously incorporates the newest observations into the proposal distribution in the update stage.In the proposed approach,likelihood measure functions involving multiple features are presented to enhance the performance of model fitting.Furthermore,the multi-feature weights are self-adaptively adjusted by a PSO algorithm throughout the tracking process.There are three main contributions.Firstly,the PSO algorithm is fused into the PF framework,which can efficiently alleviate the particles degeneracy phenomenon.Secondly,an effective convergence criterion for the PSO algorithm is explored,which can avoid particles getting stuck in local minima and maintain a greater particle diversity.Finally,a multi-feature weight self-adjusting strategy is proposed,which can significantly improve the tracking robustness and accuracy.Experiments performed on several challenging public video sequences demonstrate that the proposed tracking approach achieves a considerable performance.     

Blind estimation of phase offset in OFDM subcarrier for coherent demodulation

An adaptive algorithm named low complexity phase offset estimation (LC-POE) is proposed for orthogonal frequency division multiplexing (OFDM) signals. Depending on the requirement, the estimation procedure is divided into several scales to accelerate the adaptive convergence speed and ensure the estimation accuracy. The true phase offset is estimated through shrinking the detection range and raising the resolution scale step by step. Both the convergence performance and complexity are discussed in the paper. Simulation results show the effectiveness of the proposed algorithm. The LC-POE algorithm is promising in the application of OFDM systems.     

Improved method for the feature extraction of laser scanner using genetic clustering

Feature extraction of range images provided by ranging sensor is a key issue of pattern recognition. To automatically extract the environmental feature sensed by a 2D ranging sensor laser scanner, an improved method based on genetic clustering VGA-clustering is presented. By integrating the spatial neighbouring information of range data into fuzzy clustering algorithm, a weighted fuzzy clustering algorithm （WFCA） instead of standard clustering algorithm is introduced to realize feature extraction of laser scanner. Aimed at the unknown clustering number in advance, several validation index functions are used to estimate the validity of different clustering algorithms and one validation index is selected as the fitness function of genetic algorithm so as to determine the accurate clustering number automatically. At the same time, an improved genetic algorithm IVGA on the basis of VGA is proposed to solve the local optimum of clustering algorithm, which is implemented by increasing the population diversity and improving the genetic operators of elitist rule to enhance the local search capacity and to quicken the convergence speed. By the comparison with other algorithms, the effectiveness of the algorithm introduced is demonstrated.     

BFGS quasi-Newton location algorithm using TDOAs and GROAs

With the emergence of location-based applications in various fields,the higher accuracy of positioning is demanded.By utilizing the time differences of arrival(TDOAs) and gain ratios of arrival(GROAs),an efficient algorithm for estimating the position is proposed,which exploits the Broyden-Fletcher-Goldfarb-Shanno(BFGS) quasi-Newton method to solve nonlinear equations at the source location under the additive measurement error.Although the accuracy of two-step weighted-least-square(WLS) method based on TDOAs and GROAs is very high,this method has a high computational complexity.While the proposed approach can achieve the same accuracy and bias with the lower computational complexity when the signal-to-noise ratio(SNR) is high,especially it can achieve better accuracy and smaller bias at a lower SNR.The proposed algorithm can be applied to the actual environment due to its real-time property and good robust performance.Simulation results show that with a good initial guess to begin with,the proposed estimator converges to the true solution and achieves the Cramer-Rao lower bound(CRLB) accuracy for both near-field and far-field sources.     

Novel antenna selection algorithm based on Tanimoto similarity

A new antenna selection algorithm for multiple input multiple output （MIMO） wireless systems is proposed. The modified Tanimoto coefficient is used to compare the similarity of the rows/columns of the channel matrix. Based on the calculated similarity, the proposed algorithm chooses the antenna subset, which has the maximum product of dissimilarity and Frobenius norm. The proposed algorithm requires low computational complexity as to the optimal selection but with comparative outage capacity and average signal to noise ratio （SNR） performance. It can improve both the outage capacity and the average SNR as compared to random selection. The simulation results are shown to validate our algorithm.     

Design of PID controller with incomplete derivation based on differential evolution algorithm

To determine the optimal or near optimal parameters of PID controller with incomplete derivation, a novel design method based on differential evolution （DE） algorithm is presented. The controller is called DE-PID controller. To overcome the disadvantages of the integral performance criteria in the frequency domain such as IAE, ISE, and ITSE, a new performance criterion in the time domain is proposed. The optimization procedures employing the DE algorithm to search the optimal or near optimal PID controller parameters of a control system are demonstrated in detail. Three typical control systems are chosen to test and evaluate the adaptation and robustness of the proposed DE-PID controller. The simulation results show that the proposed approach has superior features of easy implementation, stable convergence characteristic, and good computational efficiency. Compared with the ZN, GA, and ASA, the proposed design method is indeed more efficient and robust in improving the step response of a control system.