Greedy Selection Strategy Research Articles

Ring Theory-Based Evolutionary Algorithm and its application to D{0-1} KP

In this paper, we propose a new view for designing an evolutionary algorithm by using algebraic theory to solve the combinatorial optimization problem. Using the addition, multiplication and inverse operation of the direct product of rings, we first propose two evolution operators: the global exploration operator (R-GEO) and the local development operator (R-LDO). Then, by utilizing the R-GEO and R-LDO to generate individuals and applying the greedy selection strategy to generate a new population, we propose a new algorithm – the Ring Theory-Based Evolutionary Algorithm (RTEA) – for the combinatorial optimization problem. Moreover, we give a new method for solving the discounted {0-1} knapsack problem (D{0–} KP) by using the RTEA. To verify the performance of the RTEA, we use it and existing algorithms to solve four kinds of large-scale instances of the D{0-1} KP. The computational results show that the RTEA performs better than the others, and it is more suitable for solving the D{0-1} KP problem. Moreover, it indicates that using algebraic theory to design evolutionary algorithms is feasible and effective.

A two-stage differential biogeography-based optimization algorithm and its performance analysis

Biogeography-based optimization (BBO) has drawn a lot of attention as its outstanding performance. However, same with certain typical swarm optimization algorithm, BBO severely suffers from premature convergence problem and the rotational variance of migration operator. In this paper, a two-stage differential biogeography-based optimization (TDBBO) is proposed to address the premature convergence problem and alleviate the rotational variance. In the migration operator, the emigration model is selected according to the two-stage mechanism. The constant emigration model is employed to maintain the diversity of population in the early evolutionary process. The sinusoidal emigration model is selected to accelerate the convergence speed in the late evolutionary process. Meanwhile, the BBO/current-to-select/1, which is a rotationally invariant arithmetic crossover operator, is designed to alleviate the rotational variance. The standard mutation operator is replaced by the Gaussian mutation operator to jump out the local optimum effectively. The greedy selection strategy is introduced to accelerate the convergence speed after the migration and the mutation operators. Besides, the convergence performance of TDBBO is analyzed with the Markov model. Compared with the standard BBO and other outstanding BBO variants on CEC 2017 benchmarks, the TDBBO is superior to the state-of-art BBO variants in terms of solution quality, convergence speed and stability. The TDBBO lays a solid foundation for solving optimization problems of expert and intelligent systems.

An efficient modified grey wolf optimizer with Lévy flight for optimization tasks

The grey wolf optimizer (GWO) is a new efficient population-based optimizer. The GWO algorithm can reveal an efficient performance compared to other well-established optimizers. However, because of the insufficient diversity of wolves in some cases, a problem of concern is that the GWO can still be prone to stagnation at local optima. In this article, an improved modified GWO algorithm is proposed for solving either global or real-world optimization problems. In order to boost the efficacy of GWO, Lévy flight (LF) and greedy selection strategies are integrated with the modified hunting phases. LF is a class of scale-free walks with randomly-oriented steps according to the Lévy distribution. In order to investigate the effectiveness of the modified Lévy-embedded GWO (LGWO), it was compared with several state-of-the-art optimizers on 29 unconstrained test beds. Furthermore, 30 artificial and 14 real-world problems from CEC2014 and CEC2011 were employed to evaluate the LGWO algorithm. Also, statistical tests were employed to investigate the significance of the results. Experimental results and statistical tests demonstrate that the performance of LGWO is significantly better than GWO and other analyzed optimizers.

Krill herd algorithm based on cuckoo search for solving engineering optimization problems

This paper presents a hybrid krill herd (CSKH) approach to solve structural optimization problems. CSKH improved the Krill herd algorithm (KH) by combining KU/KA operator originated from cuckoo search algorithm (CS) with KH. In CSKH, a greedy selection scheme is used and often overtakes the original KH and CS. In addition, in order to further enhance the assessment of CSKH, a fraction of the worst krill is thrown away and substituted with newly randomly generated ones by KA operator at the end of each generation. The CSKH is applied to five real engineering problems to verify its performance. The experimental results have proven that CSKH algorithm is well capable of solving constrained engineering design problems more efficiently and effectively than the basic CS and KH algorithm.

Differential evolution with improved individual-based parameter setting and selection strategy

In this paper, a novel differential evolution (DE) algorithm is proposed to improve the search efficiency of DE by employing the information of individuals to adaptively set the parameters of DE and update population. Firstly, a combined mutation strategy is developed by using two mixed mutation strategies with a prescribed probability. Secondly, the fitness values of original and guiding individuals are used to guide the parameter setting. Finally, a diversity-based selection strategy is designed by assembling greedy selection strategy and defining a new weighted fitness value based on the fitness values and positions of target and trial individuals. The proposed algorithm compares with eight existing algorithms on CEC 2005 and 2014 contest test instances, and is applied to solve the Spread Spectrum Radar Polly Code Design. Experimental results show that the proposed algorithm is very competitive.

Partial Hash Update via Hamming Subspace Learning.

Hashing technique has become an effective method for information retrieval due to the fast calculation of the Hamming distance. However, with the continuous growth of data coming from the Internet, the online update of hashing on the massive social data becomes very time-consuming. To alleviate this issue, in this paper, we propose a novel updating technique for hashing methods, namely Hamming Subspace Learning (HSL). The motivation of HSL is to generate a low-dimensional Hamming subspace from a high-dimensional Hamming space by selecting representative hash functions. Through HSL, we aim to improve the speed of updating binary codes for all samples. We present a method for Hamming subspace learning based on greedy selection strategy and the Distribution Preserving Hamming Subspace learning (DHSL) algorithm by designing a novel loss function. The experimental results demonstrate that the HSL is effective to improve the speed of online updating and the performance of hashing algorithm.

Gradual and Cumulative Improvements to the Classical Differential Evolution Scheme through Experiments

Abstract The paper presents the experimental results of some tests conducted with the purpose to gradually and cumulatively improve the classical DE scheme in both efficiency and success rate. The modifications consisted in the randomization of the scaling factor (a simple jitter scheme), a more efficient Random Greedy Selection scheme, an adaptive scheme for the crossover probability and a resetting mechanism for the agents. After each modification step, experiments have been conducted on a set of 11 scalable, multimodal, continuous optimization functions in order to analyze the improvements and decide the new improvement direction. Finally, only the initial classical scheme and the constructed Fast Self-Adaptive DE (FSA-DE) variant were compared with the purpose of testing their performance degradation with the increase of the search space dimension. The experimental results demonstrated the superiority of the proposed FSA-DE variant.

Predefined Redundant Dictionary for Effective Depth Maps Representation

The multi-view video plus depth (MVD) video format consists of two components: texture and depth map, where a combination of these components enables a receiver to generate arbitrary virtual views. However, MVD presents a very voluminous video format that requires a compression process for storage and especially for transmission. Conventional codecs are perfectly efficient for texture images compression but not for intrinsic depth maps properties. Depth images indeed are characterized by areas of smoothly varying grey levels separated by sharp discontinuities at the position of object boundaries. Preserving these characteristics is important to enable high quality view synthesis at the receiver side. In this paper, sparse representation of depth maps is discussed. It is shown that a significant gain in sparsity is achieved when particular mixed dictionaries are used for approximating these types of images with greedy selection strategies. Experiments are conducted to confirm the effectiveness at producing sparse representations, and competitiveness, with respect to candidate state-of-art dictionaries. Finally, the resulting method is shown to be effective for depth maps compression and represents an advantage over the ongoing 3D high efficiency video coding compression standard, particularly at medium and high bitrates.

Approximation of eigenfunctions in kernel-based spaces

Kernel-based methods in Numerical Analysis have the advantage of yielding optimal recovery processes in the "native" Hilbert space $\calh$ in which they are reproducing. Continuous kernels on compact domains have an expansion into eigenfunctions that are both $L_2$-orthonormal and orthogonal in $\calh$ (Mercer expansion). This paper examines the corresponding eigenspaces and proves that they have optimality properties among all other subspaces of $\calh$. These results have strong connections to $n$-widths in Approximation Theory, and they establish that errors of optimal approximations are closely related to the decay of the eigenvalues. Though the eigenspaces and eigenvalues are not readily available, they can be well approximated using the standard $n$-dimensional subspaces spanned by translates of the kernel with respect to $n$ nodes or centers. We give error bounds for the numerical approximation of the eigensystem via such subspaces. A series of examples shows that our numerical technique via a greedy point selection strategy allows to calculate the eigensystems with good accuracy.

Using Learning Classifier Systems to Learn Stochastic Decision Policies

To solve reinforcement learning problems, many learning classifier systems (LCSs) are designed to learn state-action value functions through a compact set of maximally general and accurate rules. Most of these systems focus primarily on learning deterministic policies by using a greedy action selection strategy. However, in practice, it may be more flexible and desirable to learn stochastic policies, which can be considered as direct extensions of their deterministic counterparts. In this paper, we aim to achieve this goal by extending each rule with a new policy parameter. Meanwhile, a new method for adaptive learning of stochastic action selection strategies based on a policy gradient framework has also been introduced. Using this method, we have developed two new learning systems, one based on a regular gradient learning technology and the other based on a new natural gradient learning method. Both learning systems have been evaluated on three different types of reinforcement learning problems. The promising performance of the two systems clearly shows that LCSs provide a suitable platform for efficient and reliable learning of stochastic policies.

A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization

This paper suggests a dynamic multi-colony multi-objective artificial bee colony algorithm (DMCMOABC) by using the multi-deme model and a dynamic information exchange strategy. In the proposed algorithm, K colonies search independently most of the time and share information occasionally. In each colony, there are S bees containing equal number of employed bees and onlooker bees. For each food source, the employed or onlooker bee will explore a temporary position generated by using neighboring information, and the better one determined by a greedy selection strategy is kept for the next iterations. The external archive is employed to store non-dominated solutions found during the search process, and the diversity over the archived individuals is maintained by using crowding-distance strategy. If a randomly generated number is smaller than the migration rate R, then an elite, defined as the intermediate individual with the maximum crowding-distance value, is identified and used to replace the worst food source in a randomly selected colony. The proposed DMCMOABC is evaluated on a set of unconstrained/constrained test functions taken from the CEC2009 special session and competition in terms of four commonly used metrics EPSILON, HV, IGD and SPREAD, and it is compared with other state-of-the-art algorithms by applying Friedman test on the mean of IGD. The test results show that DMCMOABC is significantly better than or at least comparable to its competitors for both unconstrained and constrained problems.

Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm

Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.

Compact Aspect Embedding for Diversified Query Expansions

Diversified query expansion (DQE) based approaches aim to select a set of expansion terms with less redundancy among them while covering as many query aspects as possible. Recently they have experimentally demonstrate their effectiveness for the task of search result diversification. One challenge faced by existing DQE approaches is how to ensure the aspect coverage. In this paper, we propose a novel method for DQE, called compact aspect embedding, which exploits trace norm regularization to learn a low rank vector space for the query, with each eigenvector of the learnt vector space representing an aspect, and the absolute value of its corresponding eigenvalue representing the association strength of that aspect to the query. Meanwhile, each expansion term is mapped into the vector space as well. Based on this novel representation of the query aspects and expansion terms, we design a greedy selection strategy to choose a set of expansion terms to explicitly cover all possible aspects of the query.We test our method on several TREC diversification data sets, and show that our method significantly outperforms the state-of-the-art search result diversification approaches.

Forward–Backward Greedy Algorithms for Atomic Norm Regularization

In many signal processing applications, the aim is to reconstruct a signal that has a simple representation with respect to a certain basis or frame. Fundamental elements of the basis known as "atoms" allow us to define "atomic norms" that can be used to formulate convex regularizations for the reconstruction problem. Efficient algorithms are available to solve these formulations in certain special cases, but an approach that works well for general atomic norms, both in terms of speed and reconstruction accuracy, remains to be found. This paper describes an optimization algorithm called CoGEnT that produces solutions with succinct atomic representations for reconstruction problems, generally formulated with atomic-norm constraints. CoGEnT combines a greedy selection scheme based on the conditional gradient approach with a backward (or "truncation") step that exploits the quadratic nature of the objective to reduce the basis size. We establish convergence properties and validate the algorithm via extensive numerical experiments on a suite of signal processing applications. Our algorithm and analysis also allow for inexact forward steps and for occasional enhancements of the current representation to be performed. CoGEnT can outperform the basic conditional gradient method, and indeed many methods that are tailored to specific applications, when the enhancement and truncation steps are defined appropriately. We also introduce several novel applications that are enabled by the atomic-norm framework, including tensor completion, moment problems in signal processing, and graph deconvolution.

Multi-hive bee foraging algorithm for multi-objective optimal power flow considering the cost, loss, and emission

This paper proposes a multi-hive multi-objective bee algorithm (M2OBA) for optimal power flow (OPF) in power systems. The proposed M2OBA extend original artificial bee colony (ABC) algorithm to multi-objective and cooperative mode by combining external archive, comprehensive learning, greedy selection, crowding distance, and cooperative search strategy. Our algorithm uses the concept of Pareto dominance and comprehensive learning mechanism to determine the flight direction of a bee and maintains nondominated solution vectors in external archive based on greedy selection and crowing distance strategies. With cooperative search approaches, the single population ABC has been extended to interacting multi-hive model by constructing colony-level interaction topology and information exchange strategies. With six mathematical benchmark functions, M2OBA is proved to have significantly better performance than three successful multi-objective optimizers, namely the fast non-dominated sorting genetic algorithm (NSGA-II), the multi-objective particle swarm optimizer (MOPSO), and the multi-objective ABC (MOABC), for solving complex multi-objective optimization problems. M2OBA is then used for solving the real-world OPF problem that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results, which are also compared to NSGA-II, MOPSO, and MOABC, are presented to illustrate the effectiveness and robustness of the proposed method.

An Artificial Bee Colony Algorithm for Multiobjective Redundancy Allocation Problem in Repairable System

This paper proposed a multiobjective artificial bee colony algorithm (MOABC) to solve the reliability redundancy allocation problem (RAP) with multiple objectives, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. The number of redundancy components is to be decided so as to maximize the availability and minimize the designing cost of the system simultaneously. It shows that the proposed algorithm can solve multiobjective redundancy allocation problem efficiently.

An Artificial Bee Colony Algorithm for Multiobjective Redundancy Allocation Problem in Repairable System

This paper proposed a multiobjective artificial bee colony algorithm (MOABC) to solve the reliability redundancy allocation problem (RAP) with multiple objectives, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. The number of redundancy components is to be decided so as to maximize the availability and minimize the designing cost of the system simultaneously. It shows that the proposed algorithm can solve multiobjective redundancy allocation problem efficiently.

Adaptive matching pursuit using coordinate descent and double residual minimization

We present a greedy recursive algorithm for computing sparse solutions to systems of linear equations. Derived from adaptive matching pursuit, the algorithm employs a greedy column selection strategy which, combined with coefficient update via coordinate descent, ensures a low complexity. The sparsity level is estimated online using the predictive least squares (PLS) criterion. The key to performance is the minimization of two residuals, corresponding to two solutions with different sparsity levels, one for finding the values of the nonzero coefficients, the other for maintaining a large enough pool of candidates for the PLS criterion. We test the algorithm for a sparse time-varying finite impulse response channel; the performance is comparable with or better than that of the competing methods, while the complexity is lower.

Role Mining with Probabilistic Models

Role mining tackles the problem of finding a role-based access control (RBAC) configuration, given an access-control matrix assigning users to access permissions as input. Most role-mining approaches work by constructing a large set of candidate roles and use a greedy selection strategy to iteratively pick a small subset such that the differences between the resulting RBAC configuration and the access control matrix are minimized. In this article, we advocate an alternative approach that recasts role mining as an inference problem rather than a lossy compression problem. Instead of using combinatorial algorithms to minimize the number of roles needed to represent the access-control matrix, we derive probabilistic models to learn the RBAC configuration that most likely underlies the given matrix. Our models are generative in that they reflect the way that permissions are assigned to users in a given RBAC configuration. We additionally model how user-permission assignments that conflict with an RBAC configuration emerge and we investigate the influence of constraints on role hierarchies and on the number of assignments. In experiments with access-control matrices from real-world enterprises, we compare our proposed models with other role-mining methods. Our results show that our probabilistic models infer roles that generalize well to new system users for a wide variety of data, while other models’ generalization abilities depend on the dataset given.

Sparse representation of astronomical images

Sparse representation of astronomical images is discussed. It is shown that a significant gain in sparsity is achieved when particular mixed dictionaries are used for approximating these types of images with greedy selection strategies. Experiments are conducted to confirm (i) the effectiveness at producing sparse representations and (ii) competitiveness, with respect to the time required to process large images. The latter is a consequence of the suitability of the proposed dictionaries for approximating images in partitions of small blocks. This feature makes it possible to apply the effective greedy selection technique called orthogonal matching pursuit, up to some block size. For blocks exceeding that size, a refinement of the original matching pursuit approach is considered. The resulting method is termed "self-projected matching pursuit," because it is shown to be effective for implementing, via matching pursuit itself, the optional backprojection intermediate steps in that approach.