EC Algorithms Research Articles

Efficient Clustering Based Routing for Energy Management in Wireless Sensor Network-Assisted Internet of Things

Wireless sensor networks (WSNs) play a huge part in arising innovations like smart applications, the Internet of Things, and numerous self-designed, independent applications. Energy exhaustion and efficient energy consumption are principal issues in wireless sensor networks. Energy is a significant and valuable asset of sensor nodes; early energy depletion ultimately leads to a shorter network lifetime and the replacement of sensor nodes. This research proposes a novel Power-Efficient Cluster-based Routing (PECR) algorithm. It takes in clustering using K-Means, the arrangement of Cluster Heads (CHs) and a Main Cluster Head (MCH), the optimal route choice, communication in light of the energy utilization model, cluster heads, and main cluster head alternation based on residual energy and relative location. PECR decreases traffic overburden, restricts energy usage, and at last, expands the network lifetime. Sensor nodes sense the information and transmit traffic to a Base Station (BS) through a legitimate channel. The results confirm it decreases the traffic overhead and effectively utilizes the energy assets. The simulation results show that PECR’s performance is 44% more improved than LEACH, EC, EECRP, and EECA algorithms. It is suitable for networks that require a stretched life.

A multi-objective evolutionary algorithm with interval based initialization and self-adaptive crossover operator for large-scale feature selection in classification

Feature selection (FS) is an important data pre-processing technique in classification. In most cases, FS can improve classification accuracy and reduce feature dimension, so it can be regarded as a multi-objective optimization problem. Many evolutionary computation techniques have been applied to FS problems and achieved good results. However, an increase in data dimension means that search difficulty also greatly increases, and EC algorithms with insufficient search ability maybe only find sub-optimal solutions in high probability. Moreover, an improper initial population may negatively affect the convergence speed of algorithms. To solve the problems highlighted above, this paper proposes MOEA-ISa: a multi-objective evolutionary algorithm with interval based initialization and self-adaptive crossover operator for large-scale FS. The proposed interval based initialization can limit the number of selected features for solution to improve the distribution of the initial population in the target space and reduce the similarity of the initial population in the decision space. The proposed self-adaptive crossover operator can determine the number of nonzero genes in offspring according to the similarity of parents, and it combines with the feature weights obtained by ReliefF method to improve the quality of offspring. In the experiments, the proposed algorithm was compared with six other algorithms on 13 benchmark UCI datasets and two benchmark LIBSVM datasets, and an ablation experiment was performed on MOEA-ISa. The results show that MOEA-ISa’s performance is better than the six other algorithms for solving large-scale FS problems, and the proposed interval based initialization and self-adaptive crossover operator can effectively improve the performance of MOEA-ISa. The source code of MOEA-ISa is available on GitHub at https://github.com/xueyunuist/MOEA-ISa.

A self-adaptive weighted differential evolution approach for large-scale feature selection

Recently, many evolutionary computation methods have been developed to solve the feature selection problem. However, the studies focused mainly on small-scale issues, resulting in stagnation issues in local optima and numerical instability when dealing with large-scale feature selection dilemmas. To address these challenges, this paper proposes a novel weighted differential evolution algorithm based on self-adaptive mechanism, named SaWDE, to solve large-scale feature selection. First, a multi-population mechanism is adopted to enhance the diversity of the population. Then, we propose a new self-adaptive mechanism that selects several strategies from a strategy pool to capture the diverse characteristics of the datasets from the historical information. Finally, a weighted model is designed to identify the important features, which enables our model to generate the most suitable feature-selection solution. We demonstrate the effectiveness of our algorithm on twelve large-scale datasets. The performance of SaWDE is superior compared to six non-EC algorithms and six other EC algorithms, on both training and test datasets and on subset size, indicating that our algorithm is a favorable tool to solve the large-scale feature selection problem. Moreover, we have experimented SaWDE with six EC algorithms on twelve higher-dimensional data, which demonstrates that SaWDE is more robust and efficient compared to those state-of-the-art methods. SaWDE source code is available on Github at https://github.com/wangxb96/SaWDE.

A New Population Initialization of Particle Swarm Optimization Method Based on PCA for Feature Selection

In many fields such as signal processing, machine learning, pattern recognition and data mining, it is common practice to process datasets containing huge numbers of features. In such cases, Feature Selection (FS) is often involved. Meanwhile, owing to their excellent global search ability, evolutionary computation techniques have been widely employed to the FS. So, as a powerful global search method and calculation fast than other EC algorithms, PSO can solve features selection problems well. However, when facing a large number of feature selection, the efficiency of PSO drops significantly. Therefore, plenty of works have been done to improve this situation. Besides, many studies have shown that an appropriate population initialization can effectively help to improve this problem. So, basing on PSO, this paper introduces a new feature selection method with filter-based population. The proposed algorithm uses Principal Component Analysis (PCA) to measure the importance of features first, then based on the sorted feature information, a population initialization method using the threshold selection and the mixed initialization is proposed. The experiments were performed on several datasets and compared to several other related algorithms. Experimental results show that the accuracy of PSO to solve feature selection problems is significantly improved after using proposed method.

Novel Combined Variable Selection Approach Using Memetic Algorithm With Complex Harmonic Regularization

Variable selection has been highly successful in big data analyses, and regularization approaches are commonly used methods, which can automatically select important variables while constructing machine learning models. Due to the real datasets have complex relationships between relevant variable groups, many group-sparsity regularization approaches are proposed recently. However, these approaches usually are non-convex and sensitive to hyper-parameters. Therefore, optimization of the regularizations is a challenging task. In this paper, we present a novel memetic algorithm with the complex harmonic regularization (MA-CHR), which combines EC algorithm for hyper-parameters (global search) and complex harmonic regularization with path seeking strategy for the group-sparsity variable selection (local search). We further introduced a novel genetic individual representation (intron+exon) to efficient obtain the global optimal solution of this group-sparsity regularization. Simulation and five real data experiments demonstrate that the proposed MA-CHR method performs better than the state-of-the-art regularization methods in selecting groups of relevant variables and classification.

Proposed adaptive joint error‐resilience concealment algorithms for efficient colour‐plus‐depth 3D video transmission

The authors propose efficient hybrid error resilience and error concealment (ER-EC) algorithms for H.264 3D video-plus-depth (3DV + D) transmission over error-prone channels. At the encoder, content-adaptive pre-processing ER mechanisms are implemented by applying the context adaptive variable length coding (CAVLC), the slice structured coding, and the explicit flexible macro-block ordering. At the decoder, a post-processing EC algorithm with multi-proposition schemes is implemented to recover the lost 3DV colour frames. The convenient EC hypothesis is adopted based on the lost macro-blocks size mode, the faulty view, and the frame types. For the recovery of the lost 3DV depth frames, an encoder-independent decoder-dependent depth-assisted EC algorithm is suggested. It exploits the previously estimated colour disparity vectors (DVs) and motion vectors (MVs) to estimate more additional depth-assisted MVs and DVs. After that, the optimum colour-plus-depth DVs and MVs are accurately selected by employing the directional interpolation EC algorithm and the decoder MV estimation algorithm. Finally, a weighted overlapping block motion and disparity compensation scheme is utilised to reinforce the performance of the proposed hybrid ER-EC algorithms. Experimental results on standard 3DV + D sequences show that the proposed hybrid algorithms have superior objective and subjective performance indices.

Multi-layer Explosion Based Fireworks Algorithm

We propose a new multi-layer explosion strategy inspired by various explosion patterns of real reworks to accelerate reworks algorithm (FWA). Each rework individual conducts multiple explosions to explore a local fitness landscape carefully instead of a single layer explosion used in canonical FWA. In the proposal, each rework individual generates a small number of sparks in the first layer randomly, then the generated sparks conduct the second layer explosions to generate new diverse sparks. These new sparks repeat the above operations until the number of this iteration reaches the predefined maximum layer number. Theoretically, the number of explosion layers can be set to any positive integer, and the proposed strategy expects to generate various potential sparks using the multi-layer explosion strategy without changing the total number of generated sparks. The proposed strategy can combine with not only basic FWA but also other versions of FWA algorithms easily and replace their corresponding explosion operations to develop a new version, multi-layer explosion-based FWA. To evaluate the performance of our proposal, we select a more powerful variant of FWA, Enhanced FWA (EFWA) as the baseline algorithm and combine with our proposed explosion strategy. We run our proposal on 28 benchmark functions from CEC2013 test suites of 2-dimensions (2-D), 10-D and 30-D with 30 trial runs and compare with several state-of-theart EC algorithms. The experimental results confirm that the proposed strategy is effective and promising, which can obtain a better performance for FWA in terms of convergence speed and convergence accuracy. We finally analyze composition as well as feasibility of proposal and list some open topics.

An improved cuckoo search algorithm for integer programming problems

This paper presents a novel cuckoo search algorithm called elite opposition - cuckoo search algorithm (ECS) for solving integer programming problems. The opposite solution of the elite individual in the population is generated by an opposition-based strategy in the proposed algorithm and form an opposite search space by constructing the opposite population that locates inside the dynamic search boundaries, then, the search space of the algorithm is guided to approximate the space in which the global optimum is included by simultaneously evaluating the current population and the opposite one. The results show that ECS algorithm has faster convergence speed, higher computational precision and is more effective for solving integer programming problems.

From determinism and probability to chaos: chaotic evolution towards philosophy and methodology of chaotic optimization.

We present and discuss philosophy and methodology of chaotic evolution that is theoretically supported by chaos theory. We introduce four chaotic systems, that is, logistic map, tent map, Gaussian map, and Hénon map, in a well-designed chaotic evolution algorithm framework to implement several chaotic evolution (CE) algorithms. By comparing our previous proposed CE algorithm with logistic map and two canonical differential evolution (DE) algorithms, we analyse and discuss optimization performance of CE algorithm. An investigation on the relationship between optimization capability of CE algorithm and distribution characteristic of chaotic system is conducted and analysed. From evaluation result, we find that distribution of chaotic system is an essential factor to influence optimization performance of CE algorithm. We propose a new interactive EC (IEC) algorithm, interactive chaotic evolution (ICE) that replaces fitness function with a real human in CE algorithm framework. There is a paired comparison-based mechanism behind CE search scheme in nature. A simulation experimental evaluation is conducted with a pseudo-IEC user to evaluate our proposed ICE algorithm. The evaluation result indicates that ICE algorithm can obtain a significant better performance than or the same performance as interactive DE. Some open topics on CE, ICE, fusion of these optimization techniques, algorithmic notation, and others are presented and discussed.

Temporal Error Concealment Algorithm with Improved Ability on Motion Vector Recovery for H.264/AVC Decoder

During network transmissions, packet loss cannot be avoided, which leads to the decrease of the video quality. In order to provide better quality under unreliable communication environments, an efficient motion vector (MV) recovery algorithm is proposed in this paper for video temporal error concealment (TEC) with H.264/AVC standard. To increase the accuracy, 4 × 4 block size is regarded as the basic operation unit in the proposal, and the detailed processing procedures of the proposed algorithm for three cases of packet loss which happen frequently are separately discussed. Simulation results show that the proposed algorithm could achieve better subjective and objective qualities with less time-cost compared with the existing temporal EC algorithms. With its simple composition, it is could be easily used in multimedia communication systems with H.264/AVC coding standard.

Evolutionary algorithms for data mining

Artificial evolution can be applied to machine learning thanks to genetic programming, for instance, which is a very successful branch of Evolutionary Computing. One would therefore think that by transitivity, because data-mining is one of the main applications of machine learning, artificial evolution could be successful in solving data-mining problems. Whether this is the case or not, it seems that artificial evolution is not much used in data-mining, even though many papers show that EC can provide interesting alternative solutions to standard machine learning approaches. However, a secret weapon of EC is that it is intrinsically parallel. This very special feature allows EC to exploit multi-core computers as well as the massive parallelism of GPGPU cards, with their hundreds of cores. Then, the island model makes it possible to obtain scalable linear (an even in some cases supra-linear) speedup when several machines are used to solve the same problem, therefore allowing one to efficiently execute EC algorithms on massively parallel machines and super-computers. These architectures were quite rare in the past, but are now widely available as parallelism is the path followed by hardware manufacturers to increase the computing power of their processors. Therefore, the future of evolutionary computation is quite bright, and any new developments in EC could probably overtake more standard methods if these methods are sequential by nature, or do not parallelize well. This is a reason why it is essential to encourage researchers to tackle problems in new domains where EC may not yet be a major actor.

ESC: Estimation of selecting core for reducing multicast delay variation under delay constraints

AbstractWith the spread of multimedia group applications, the construction of multicast trees satisfying the Quality of Service (QoS) requirements becomes a problem of prime importance. A principal factor of these real‐time applications is to optimize the delay‐ and delay variation‐bounded multicast tree (DVBMT) problem. This problem is to satisfy the minimum delay variation and the end‐to‐end delay within an upper bound. The DVBMT problem is known as an NP‐complete problem. The representative algorithms are the DVMA, the DDVCA, and the ECS algorithm. In this paper, we show that the proposed ESC algorithm outperforms the DDVCA and the ECS algorithm. The efficiency of our algorithm is verified through performance evaluation and the enhancement is up to about 19.6% in terms of normalized surcharge for multicast delay variation. The time complexity of our algorithm is O(mn2), which is comparable to the well‐known DDVCA. Copyright © 2010 John Wiley & Sons, Ltd.

Simulation Research of Extension Control Based on Crane-Double Pendulum System

<!-- /* Font Definitions */ @font-face {font-family:??; panose-1:2 1 6 0 3 1 1 1 1 1; mso-font-alt:SimSun; mso-font-charset:134; mso-generic-font-family:auto; mso-font-pitch:variable; mso-font-signature:3 135135232 16 0 262145 0;} @font-face {font-family:

Algebraic reduction of electrical multiterminal networks

An electrical dc network consisting of m edges with given conductances and of n points, n 1 of which are marked as terminal points, can be reduced to an equivalent network with connections between the n 1 terminals only (terminal network). In the special case n 1 = 2, rduction of the given network results in one single conductance. The computation of the n 1 2 replacement conductances as functions of the given conductances requires the inversion of matrices with variable coefficients. 1. A theorem (forest theorem) is proved which reduces the algebraic calculation to a combinatorial problem, and 2. a recursive algorithm (ec algorithm) is developed which simplifies stepwise the given network, thus leading to terminal networks. For the sake of simplicity all considerations have been formulated in the terminology of Ohmic networks with conductances. Generalization to passive ac networks and admittances should be obvious.