Fast Local Search Research Articles

A Differential Evolution and Spatial Distribution based Local Search for Training Fuzzy Wavelet Neural Network

Abstract Many parameter-tuning algorithms have been proposed for training Fuzzy Wavelet Neural Networks (FWNNs). Absence of appropriate structure, convergence to local optima and low speed in learning algorithms are deficiencies of FWNNs in previous studies. In this paper, a Memetic Algorithm (MA) is introduced to train FWNN for addressing aforementioned learning lacks. Differential Evolution (DE) is utilized as the global search. The main contributions of this paper are: (i) Proposing a new fast and effective local search based on spatial distribution (that is named Spatial Distribution Local Search (SDLS)), SDLS can adjust the step size of parameters adaptively toward obtaining the better ones. (ii) Introducing an adaptive selection method to select appropriate individuals from current population for local refinement in MA. (iii) Improving the selection operator in standard DE by an adaptive strategy. In this strategy, worse offspring has a chance to be replaced with its parent to prevent trapping in local optima and controlling the selection pressure. The proposed MA is compared with several training algorithms of FWNNs over some benchmark problems. Experimental results obtained, confirm the effectiveness of the proposed MA for improving the convergence rate and modeling accuracy in comparison to the other training methods.

Adaptive Coordinated Voltage Control—Part I: Basic Scheme

Based on offline studies and past experiences, a novel adaptive coordinated voltage control (ACVC) strategy is proposed in this two-part paper. An efficient adaptive response is realized by searching in a limited solution space which is defined by the prepared system knowledge. The system knowledge is acquired from offline studies and then further accumulated from online experiences by learning. With the prepared control knowledge, the search solution space can be significantly reduced. Hence, the online search can quickly achieve a good system performance. In this first part of the paper, the offline study and online control for prepared faults are presented. For some anticipated faults, an offline search is applied to get knowledge for preparation. When an emergency happens, it is compared with the knowledge in the database. If full control information is found, a fast local search is applied within the solution space. Simulation results on the New England 39-bus power system show that this ACVC strategy is very efficient for real-time applications. The learning technique and online knowledge accumulation will be presented in Part II.

A Margin-based Model with a Fast Local Searchnewline for Rule Weighting and Reduction in Fuzzynewline Rule-based Classification Systems

Fuzzy Rule-Based Classification Systems (FRBCS) are highly investigated by researchers due to their noise-stability and interpretability. Unfortunately, generating a rule-base which is sufficiently both accurate and interpretable, is a hard process. Rule weighting is one of the approaches to improve the accuracy of a pre-generated rule-base without modifying the original rules. Most of the proposed methods by now, may over-fit on training data due to generating complex decision boundaries. In this paper, a margin-based optimization model is proposed to improve the performance on unseen data. By this model, fixed-size margins are defined along the decision boundaries and the rule weights are adjusted such that the marginal space would be empty of training instances as much as possible. This model is proposed to support the single-winner reasoning method with a special cost-function to remove undesired effects of noisy instances. The model is proposed to be solved by a fast well-known local search method. With this solving method, a huge amount of irrelevant and redundant rules are removed as a side effect.Two artificial and 16 real world datasets from UCI repository are used to show that the proposed method significantly outperforms other methods with proper choice of the margin size, which is the single parameter of this method.

An Efficient Memetic Algorithm for theMax-Bisection Problem

The max-bisection problem consists in partitioning the vertices of a weighted undirected graph into two equally sized subsets so as to maximize the sum of the weights of crossing edges. It is an NP-hard combinatorial optimization problem that arises in many applications. In this paper, we present a memetic algorithm for the max-bisection problem, which integrates a new fast local search procedure, a crossover operator, and a pool updating strategy. These strategies achieve a balance between intensification and diversification. Extensive experiments were performed on a number of benchmark instances with 800 to 10,000 vertices from the literature. The proposed memetic algorithm improved the best known solutions for all benchmark instances tested in this paper. The improvement in terms of cut value over the CirCut by Burer et al. ranging from 0.02 to 4.15 percent, and the average time of our proposed memetic algorithm is much lower than that of CirCut. It shows that the proposed memetic algorithm can find high quality solutions in an acceptable running time.

GESPAR: Efficient Phase Retrieval of Sparse Signals

We consider the problem of phase retrieval, namely, recovery of a signal from the magnitude of its Fourier transform, or of any other linear transform. Due to the loss of Fourier phase information, this problem is ill-posed. Therefore, prior information on the signal is needed in order to enable its recovery. In this work we consider the case in which the signal is known to be sparse, i.e., it consists of a small number of nonzero elements in an appropriate basis. We propose a fast local search method for recovering a sparse signal from measurements of its Fourier transform (or other linear transform) magnitude which we refer to as GESPAR: GrEedy Sparse PhAse Retrieval. Our algorithm does not require matrix lifting, unlike previous approaches, and therefore is potentially suitable for large scale problems such as images. Simulation results indicate that GESPAR is fast and more accurate than existing techniques in a variety of settings.

AEDB protocol tuning with a fast efficient parallel multi-objective local search

Mobile ad hoc networks (MANETs) are infrastructure-less communication networks spontaneously created by a number of mobile devices. Due to its highly fluctuating topology, finding the optimal configuration of communication protocols is a complex and crucial task. Additionally, different objectives must be usually considered. In our previous work, we introduced a novel parallel multi-objective local search to optimise the AEDB broadcasting protocol in terms of coverage, energy used, broadcasting time, and network resources. In this paper, we extend our previous work by studying three different versions of the problem, that are restricting the minimum coverage value for valid solutions, in order to accelerate the search and make it focus on the desired area of the search space. Additionally, the convergence speed of the algorithm was analysed, and different population initialisation techniques were compared. The new algorithm is validated vs. efficient state-of-the art multi-objective evolutionary techniques, offering high quality results in much shorter times, especially for high coverage solutions.

An Improved Genetic Algorithm for the Multi Level Uncapacitated Facility Location Problem

In this paper, an improved genetic algorithm (GA) for solving the multi-level uncapacitated facility location problem (MLUFLP) is presented. First improvement is achieved by better implementation of dynamic programming, which speeds up the running time of the overall GA implementation. Second improvement is hybridization of the genetic algorithm with the fast local search procedure designed specially for MLUFLP. The experiments were carried out on instances proposed in the literature which are modied standard single level facility location problem instances. Improved genetic algorithm reaches all known optimal and the best solutions from literature, but in much shorter time. Hybridization with local search improves several best-known solutions for large-scale MLUFLP instances, in cases when the optimal is not known. Overall running time of both proposed GA methods is signicantly shorter compared to previous GA approach.

A hybrid metaheuristic for the cyclic antibandwidth problem

In this paper, we propose a hybrid metaheuristic algorithm to solve the cyclic antibandwidth problem. This hard optimization problem consists of embedding an n-vertex graph into the cycle Cn, such that the minimum distance (measured in the cycle) of adjacent vertices is maximized. It constitutes a natural extension of the well-known antibandwidth problem, and can be viewed as the dual problem of the cyclic bandwidth problem.Our method hybridizes the artificial bee colony methodology with tabu search to obtain high-quality solutions in short computational times. Artificial bee colony is a recent swarm intelligence technique based on the intelligent foraging behavior of honeybees. The performance of this algorithm is basically determined by two search strategies, an initialization scheme that is employed to construct initial solutions and a method for generating neighboring solutions. On the other hand, tabu search is an adaptive memory programming methodology introduced in the eighties to solve hard combinatorial optimization problems. Our hybrid approach adapts some elements of both methodologies, artificial bee colony and tabu search, to the cyclic antibandwidth problem. In addition, it incorporates a fast local search procedure to enhance the local intensification capability. Through the analysis of experimental results, the highly effective performance of the proposed algorithm is shown with respect to the current state-of-the-art algorithm for this problem.

A hybrid cuckoo search via Lévy flights for the permutation flow shop scheduling problem

The permutation flow shop problem (PFSSP) is an NP-hard problem of wide engineering and theoretical background. In this paper, a cuckoo search (CS)-based memetic algorithm, called HCS, is proposed for the PFSSP. To make CS suitable for the PFSSP, a largest-ranked-value (LRV)-rule-based random key is used to convert the continuous position in CS into a discrete job permutation. The Nawaz-Enscore-Ham (NEH) heuristic is then combined with the random initialisation to initialise the population with a certain quality and diversity. After that, CS is employed to evolve nest vectors for exploration, and a fast local search is embedded to enhance the local exploitation ability. In addition, simulations and comparisons based on PFSSP benchmarks are carried out, which shows that our algorithm is both effective and efficient.

A hybrid gradient‐based and differential evolution algorithm for infinite impulse response adaptive filtering

SUMMARYGlobal optimization algorithms (GO) had been applied to solve the adaptive infinite impulse response filtering problem, which is known to have multimodal error surface under certain conditions. However, although GO may be able to search multimodal surfaces, they have certain disadvantages. They may not converge to any minimum point, the convergence speed is reduced as the solution vectors move closer, and tracking ability for non‐stationary environment is lacking. The traditional gradient descent method does not have these limitation but is not able to search multimodal surfaces. In this work, we propose a hybrid algorithm combining gradient descent and differential evolution (DE) for adapting the coefficients of infinite impulse response adaptive filters. DE is run in a block‐based manner. The coefficient vector with the lowest error surface value (the best member) of the current block is updated via gradient descent for the duration of the next block. Thus combining the ability to search multimodal surface of DE and fast local search of gradient descent. As with all GO, global search capacity is gradually lost as the coefficient vectors converge together. Thus, re‐initialization is also incorporated into the hybrid algorithm to provide continuous global search capacity for non‐stationary environment. All the coefficient vectors except the best member are re‐initialized when the normalized mean Euclidean distance between each pair of vectors falls below a threshold value. Simulation results show that the proposed algorithm achieves better solution quality and convergence speed than classic DE and GO for stationary and non‐stationary environments. Copyright © 2013 John Wiley & Sons, Ltd.

A discrete artificial bee colony algorithm with composite mutation strategies for permutation flow shop scheduling problem

The Permutation Flow Shop Scheduling Problem (PFSSP) is an NP-hard problem of wide engineering and theoretical background. In this paper, a kind of discrete artificial bee colony with composite mutation strategies is presented to compensate the defects of the single mutation scheme that is easy to get into the local best for PFSSP, named CDABC. Firstly, to make ABC suitable for PFSSP, we regard each discrete job permutation as a food source and apply discrete operations to generate a new neighbourhood food source for different bees. Secondly, the Nawaz-Enscore-Ham (NEH) heuristic is combined with the random initialization to initialize the population with a certain quality and diversity. Thirdly, the composite mutation strategies are proposed to enable the DABC to solve the permutation flow shop scheduling. Finally, the fast local search is used for enhancing the best individual. Within our knowledge, there are few papers to discuss artificial bee colony algorithm about PFSSP with the objective of minimizing total flow time and maximum lateness of jobs. In this sense, our work can be viewed as a start point for researchers to develop ABC-based algorithms to solve PFSSP. Additionally, simulations and comparisons based on PFSSP benchmarks are carried out, which shows that our algorithm is very competitive. We have also evaluated our algorithm with the well known DMU problems. For the problems with the objective of minimizing makespan, the algorithm CDABC obtains 26 new upper bounds of the 40 instances, and for the problems with the objective of maximum lateness, CDABC obtains 137 new upper bounds of the 160 instances. These new upper bounds can be used for future algorithms to compare their results with ours.

An opposition-based differential evolution algorithm for permutation flow shop scheduling based on diversity measure

The permutation flow shop problem (PFSSP) is an NP-hard problem of wide engineering and theoretical background. In this paper, a differential evolution (DE) based memetic algorithm, named ODDE, is proposed for PFSSP. First, to make DE suitable for PFSSP, a new LRV rule based on random key is introduced to convert the continuous position in DE to the discrete job permutation. Second, the NEH heuristic was combined the random initialization to the population with certain quality and diversity. Third, to improve the global optimization property of DE, a DE approach based on measure of population's diversity is proposed to tuning the crossover rate. Fourth, to improve the convergence rate of DE, the opposition-based DE employs opposition-based learning for the initialization and for generation jumping to enhance the global optimal solution. Fifth, the fast local search is used for enhancing the individuals with a certain probability. Sixth, the pairwise based local search is used to enhance the global optimal solution and help the algorithm to escape from local minimum. Additionally, simulations and comparisons based on PFSSP benchmarks are carried out, which show that our algorithm is both effective and efficient. We have also evaluated our algorithm with the well known DMU problems. For the problems with the objective of minimizing makespan, the algorithm ODDE obtains 24 new upper bounds of the 40 instances, and for the problems with the objective of maximum lateness, ODDE obtains 137 new upper bounds of the 160 instances. These new upper bounds can be used for future algorithms to compare their results with ours.

A fast and effective local search algorithm for optimizing the placement of wind turbines

The placement of wind turbines on a given area of land such that the wind farm produces a maximum amount of energy is a challenging optimization problem. In this article, we tackle this problem, taking into account wake effects that are produced by the different turbines on the wind farm. We significantly improve upon existing results for the minimization of wake effects by developing a new problem-specific local search algorithm. One key step in the speed-up of our algorithm is the reduction in computation time needed to assess a given wind farm layout compared to previous approaches. Our new method allows the optimization of large real-world scenarios within a single night on a standard computer, whereas weeks on specialized computing servers were required for previous approaches.

Scheduling multi-objective open shop scheduling using a hybrid immune algorithm

Despite their importance, hardly ever have multi-objective open shop problems been the topic of researches. This paper studies the mentioned problem and proposes some novel multi-objective solution methods centered on the idea behind artificial immune and simulated annealing algorithms incorporating with powerful and fast local search engines. First, the algorithms are tuned and then carefully evaluated for their performance by means of multi-objective performance measures and statistical tools. An available ant colony optimization is also brought into the experiment. Among the proposed algorithms, the results show that the variant of enhanced artificial immune algorithm outperforms the others.

A Hybrid Algorithm for Open Vehicle Routing Optimization of Coal Mines Material

This study primarily focuses on solving a practical OVRP by applying a novel hybrid adaptive Genetic Algorithm and Tabu Search (GA-TS). The GA-TS combines the GA's parallel computing and global optimization with TS's Tabu search skill and fast local search. In the GA-TS Algorithm, individuals of population do TS local search with a certain degree of probability using natural number coding. It not only ensures global optimization, but also improves the speed of computing. The Algorithm is applied to optimize the mines material transport vehicle routing of China Zhengzhou Coal Mine and power Supply Co., Ltd. Through the applications, the optimal economical transport routing has been set up and economic costs have been greatly reduced.

MOEA/D-SQA: a multi-objective memetic algorithm based on decomposition

A multi-objective memetic algorithm based on decomposition is proposed in this article, in which a simplified quadratic approximation (SQA) is employed as a local search operator for enhancing the performance of a multi-objective evolutionary algorithm based on decomposition (MOEA/D). The SQA is used for a fast local search and the MOEA/D is used as the global optimizer. The multi-objective memetic algorithm based on decomposition, i.e. a hybrid of the MOEA/D with the SQA (MOEA/D-SQA), is designed to balance local versus global search strategies so as to obtain a set of diverse non-dominated solutions as quickly as possible. The emphasis of this article is placed on demonstrating how this local search scheme can improve the performance of MOEA/D for multi-objective optimization. MOEA/D-SQA has been tested on a wide set of benchmark problems with complicated Pareto set shapes. Experimental results indicate that the proposed approach performs better than MOEA/D. In addition, the results obtained are very competitive when comparing MOEA/D-SQA with other state-of-the-art techniques.

Discrete Particle Swarm Optimization Algorithm for Lot-Streaming No-Wait Flow Shop Scheduling Problem

This paper proposed discrete particle swarm optimization(DPSO) algorithm to solve lot-streaming no-wait flow shop scheduling problem(LNFSP) with the objective of the maximum completion time. The natural encoding scheme based on job permutation and newly-designed methods were adopted to produce new individuals . After the DPSO-based exploration, a efficient fast local search based on swap neighborhood structure is used to enhance the exploitation capability. Simulation results show the effectiveness of the proposed algorithms.

Fast local search for the steiner problem in graphs

We present efficient algorithms that implement four local searches for the Steiner problem in graphs: vertex insertion, vertex elimination, key-path exchange, and key-vertex elimination. In each case, we show how to find an improving solution (or prove that none exists in the neighborhood) in <it>O</it>(<it>m</it> log <it>n</it>) time on graphs with <it>n</it> vertices and <it>m</it> edges. Many of the techniques and data structures we use are relevant in the study of dynamic graphs in general, beyond Steiner trees. Besides the theoretical interest, our results have practical impact: These local searches have been shown to find good-quality solutions in practice, but high running times limited their applicability.

A FAST LOCAL SEARCH ALGORITHM USING HISTOGRAM FEATURES FOR DNA SEQUENCE DATABASE

DNA sequence search is a very important topic in bioinformatics algorithm development. However, this task usually spends much computational time to search on large DNA sequence database. In this paper, we propose an efficient hierarchical DNA sequence search algorithm to improve the search speed while the accuracy is being kept constant. For a given query DNA sequence, firstly, a fast local search algorithm using histogram features is used as a filtering mechanism before scanning the sequences in the database. An overlapping processing is newly added to improve the robustness of the algorithm. A large number of DNA sequences with low similarity will be excluded for latter searching. The Smith-Waterman algorithm is then applied to each remainder sequences. Experimental results using GenBank se- quence data show the proposed algorithm combining histogram information and Smith-Waterman algorithm is more efficient for DNA se- quence search. Keywords- Fast search, DNA sequence, Histogram feature, Smith-Waterman algorithm, Local search.

A Novel Hybrid Monotonic Local Search Algorithm for FIR Filter Coefficients Optimization

This work presents a formulation of the FIR filter problem with sum of power-of-two (POT) coefficients as a mixed integer linear problem and solves it heuristically. The optimization problem is formulated to minimize the number of nonzero bits in each coefficient without violating the filter specifications within the pass and stop bands. A novel fast and efficient local search optimization algorithm for the filter coefficients is proposed. The algorithm called POTx does not use a tree structure in contrast to conventional MILP algorithms and offers fast computation because of a presorted search space, a monotonic dedicated search space, and the use of abort conditions. The proposed approach achieves comparable reductions to nonheuristic approaches because of a hybrid allocation scheme and multiple optimization iterations. The usefulness of the proposed algorithm for low power design of FIR filters is shown through the evaluation of several benchmark filters.