Stochastic Search Technique Research Articles

Production planning and scheduling by means of artificial immune systems and particle swarm optimisation algorithms

The main task of the scheduling optimisation process in production systems is to minimise production cost, overall production time and to ensure optimal utilisation of the resources. Application of stochastic search techniques to find a feasible schedule that minimise cost and satisfy all constraints jointed with all products can bring a particular solution of the complexity problem. On the other hand, the cost and the time of an optimisation process have to reciprocate with the found schedule; otherwise the optimisation loses its meaning. The article presents two stochastic methods, based on biologically inspired techniques, applied on a scheduling optimisation process. The first one is based on the mechanism inspired by biological evolution and the one method applies the swarm intelligence. The application of methods is illustrated on a real world example of a production line.

Peak-to-average power ratio reduction in orthogonal frequency division multiplexing system using differential evolution-based partial transmit sequences scheme

A differential evolution (DE)-based partial transmit sequence (PTS) scheme for peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems has been proposed. PTS techniques can improve the PAPR statistics of an OFDM signals, but the considerable computational complexity for the required search through a high-dimensional vector space is a potential problem for the implementation in practical systems. The DE is an efficient and powerful population-based stochastic search technique for solving optimisation problems over continuous space, which has been widely applied in many scientific and engineering fields. Thus, to reduce the complexity for searching phase weight vector and to improve the PAPR statistics, the authors introduce DE, to search the optimal phase weight factors. The simulation results show that the proposed DE-based PTS obtains an excellent PAPR performance with a low computational complexity.

Enhancing genetic algorithms for dependent job scheduling in grid computing environments

Genetic Algorithms (GAs) are stochastic search techniques based on principles of natural selection and recombination that attempt to find optimal solutions in polynomial time by manipulating a population of candidate solutions. GAs have been widely used for job scheduling optimisation in both homogeneous and heterogeneous computing environments. When compared with list scheduling heuristics, GAs can potentially provide better solutions but require much longer processing time and significant experimentation to determine GA parameters. This paper presents a GA for scheduling dependent jobs in grid computing environments. A number of selection and pre-selection criteria for the GA are evaluated with an aim to improve GA performance in job scheduling optimization. A Task Matching with Data scheme is proposed as a GA mutation operator. Furthermore, the effect of the choice of heuristics for seeding the GA is investigated.

Seeker optimization algorithm for interference suppression of linear antenna arrays by controlling position-only, phase-only, and amplitude-only

In this article, a stochastic search technique based on seeker optimization algorithm (SOA) is proposed for null steering of linear antenna arrays by controlling the position-only, phase-only, and amplitude-only. The SOA is relatively new optimization algorithm based on the concept of simulating the act of humans' intelligent search with their memory, experience, and uncertainty reasoning. Several numerical examples of Chebyshev pattern with the single, multiple, and broad nulls imposed at the directions of interference are given to illustrate the performance and flexibility of the proposed algorithm. For a comparison, the nulling patterns obtained by simulated annealing (SA) and tabu search (TS) algorithms are also given. Furthermore, the results of SOA are statistically compared with those of SA and TS algorithms. The statistical results of simulations show that SOA is superior to the other compared algorithms. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.

A Stochastic Search Approach for Tool Requirement Planning

In an automated manufacturing unit, tool requirement planning is the primary function of tool management. The issues such as tool procurement, tool life inventory, use of alternative tools etc. can be addressed at a level below aggregate planning and above operational implementation. At this level, it is very essential to choose the optimum set of tools out of the alternative tools available for performing operations on various parts in a production period for minimum cost. In the present work, a model has been developed to consider the above mentioned factors and provide the optimum set of tools, out of the alternative tools available, for performing operations on a batch of parts. The performance of the three stochastic search techniques has been studied. It has been observed that the modified simulated annealing techniques are powerful techniques to provide the results in a very less number of evaluations.

XtalOpt version r7: An open-source evolutionary algorithm for crystal structure prediction

A new version of XtalOpt, a user-friendly GPL-licensed evolutionary algorithm for crystal structure prediction, is available for download from the CPC library or the XtalOpt website, http://xtalopt.openmolecules.net. The new version now supports four external geometry optimization codes (VASP, GULP, PWSCF, and CASTEP), as well as three queuing systems: PBS, SGE, SLURM, and “Local”. The local queuing system allows the geometry optimizations to be performed on the userʼs workstation if an external computational cluster is unavailable. Support for the Windows operating system has been added, and a Windows installer is provided. Numerous bugfixes and feature enhancements have been made in the new release as well. New version program summaryProgram title:XtalOptCatalogue identifier: AEGX_v2_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEGX_v2_0.htmlProgram obtainable from: CPC Program Library, Queenʼs University, Belfast, N. IrelandLicensing provisions: GPL v2.1 or later [1]No. of lines in distributed program, including test data, etc.: 125 383No. of bytes in distributed program, including test data, etc.: 11 607 415Distribution format: tar.gzProgramming language: C++Computer: PCs, workstations, or clustersOperating system: Linux, MS WindowsClassification: 7.7External routines: Qt [2], Open Babel [3], Avogadro [4], and one of: VASP [5], PWSCF [6], GULP [7], CASTEP [8]Catalogue identifier of previous version: AEGX_v1_0Journal reference of previous version: Comput. Phys. Comm. 182 (2011) 372Does the new version supersede the previous version?: YesNature of problem: Predicting the crystal structure of a system from its stoichiometry alone remains a grand challenge in computational materials science, chemistry, and physics.Solution method: Evolutionary algorithms are stochastic search techniques which use concepts from biological evolution in order to locate the global minimum of a crystalline structure on its potential energy surface. Our evolutionary algorithm, XtalOpt, is freely available for use and collaboration under the GNU Public License. See the original publication on XtalOptʼs implementation [11] for more information on the method.Reasons for new version: Since XtalOptʼs initial release in June 2010, support for additional optimizers, queuing systems, and an operating system has been added. XtalOpt can now use VASP, GULP, PWSCF, or CASTEP to perform local geometry optimizations. The queue submission code has been rewritten, and now supports running any of the above codes on ssh-accessible computer clusters that use the Portable Batch System (PBS), Sun Grid Engine (SGE), or SLURM queuing systems for managing the optimization jobs. Alternatively, geometry optimizations may be performed on the userʼs workstation using the new internal “Local” queuing system if high performance computing resources are unavailable. XtalOpt has been built and tested on the Microsoft Windows operating system (XP or later) in addition to Linux, and a Windows installer is provided. The installer includes a development version of Avogadro that contains expanded crystallography support [12] that is not available in the mainline Avogadro releases. Other notable new developments include:•LIBSSH [10] is distributed with the XtalOpt sources and used for communication with the remote clusters, eliminating the previous requirement to set up public-key authentication;•Plotting enthalpy (or energy) vs. structure number in the plot tab will trace out the history of the most stable structure as the search progresses A read-only mode has been added to allow inspection of previous searches through the user interface without connecting to a cluster or submitting new jobs;•The tutorial [13] has been rewritten to reflect the changes to the interface and the newly supported codes. Expanded sections on optimizations schemes and save/resume have been added;•The included version of SPGLIB has been updated. An option has been added to set the Cartesian tolerance of the space group detection. A new option has been added to the Progress tableʼs right-click menu that copies the selected structureʼs POSCAR formatted representation to the clipboard;•Numerous other small bugfixes/enhancements.Summary of revisions: See “Reasons for new version” above.Running time: User dependent. The program runs until stopped by the user.

Midterm Demand Prediction of Electrical Power Systems Using a New Hybrid Forecast Technique

Prediction of daily peak load for next month is an important type of medium-term load forecast (MTLF) for electrical power systems, which provides useful information for maintenance scheduling, adequacy assessment, scheduling of fuel supplies and limited energy resources, etc. However, the exclusive characteristics of daily peak load signal, such as its nonstationary, nonlinear and volatile behavior, present a number of challenges for this task. In this paper, a new hybrid forecast engine is proposed for this purpose. The proposed engine has an iterative training mechanism composed of a novel stochastic search technique and Levenberg-Marquardt (LM) learning algorithm. The effectiveness of the proposed forecast strategy is extensively evaluated based on several benchmark datasets.

A novel algorithm for branch cut phase unwrapping

Branch cut method is a powerful noise-immune algorithm for correct phase unwrapping of noisy phase maps. The shortest branch cut length promises the optimal unwrapping result of the wrapped phase maps. A new algorithm is proposed to search for the shortest branch cut length by simple exchange operation. Although the algorithm is on the basis of stochastic search techniques, it has a high probability of finding the shortest branch cut length or an approximation of it. Compared with the traditional algorithms, the algorithm is fast and competitive.

A Novel Partial Transmitting Sequence Technique for Peak-to -Average Power Ratio Reduction in Multicarrier Transmission Systems

As an attractive technique for the reduction of the peak-to-average power ratio (PAPR) of multi-carrier transmission systems is proposed. The conventional partial transmitting sequence (PTS) combining can improve the PAPR statistics of a multi-carrier transmission systems signal. However, the optimum PTS technique requires an exhaustive search over all combinations of allowed phase factors, the search complexity increases exponentially with the number of sub-blocks. In this paper, a novel PTS based on electromagnetism-like (EMPTS) algorithm with threshold control structure is proposed to search the optimal combination of phase factor. The EMPTS algorithm not only reduces the PAPR significantly, but also decreases the computational complexity. Simulation results are conducted to demonstrate that the proposed scheme can achieve a good PAPR reduction with a better convergence rate compared with the various stochastic search techniques.

FRIT for Systems with Dead-Zone and Its Application to Ultrasonic Motors

Ultrasonic motors (USMs) intrinsically have a dead-zone property which is sensitive to load changes. This paper proposes a fictitious reference iterative tuning (FRIT) method for systems with a dead-zone property such as USMs. The standard FRIT method is basically developed for linear systems and may not give a satisfactory control performance for noninvertible nonlinear systems including USMs. In contrast, the proposed FRIT method can achieve such a performance by introducing a right inverse of a dead-zone function as a dead-zone compensator. In the optimization process of FRIT, the so-called covariance matrix adaptation evolution strategy (CMA-ES) is used for simultaneously searching a dead-zone parameter as well as controller parameters. CMA-ES is a kind of stochastic multi-point search techniques and is effective for nondifferentiable and nonconvex optimization problems. Experimental results for a USM are given to show the effectiveness of the proposed method.

Computational Aspects Related to Inference in Gaussian Graphical Models With the G-Wishart Prior

We describe a comprehensive framework for performing Bayesian inference for Gaussian graphical models based on the G-Wishart prior with a special focus on efficiently including nondecomposable graphs in the model space. We develop a new approximation method to the normalizing constant of a G-Wishart distribution based on the Laplace approximation. We review recent developments in stochastic search algorithms and propose a new method, the mode oriented stochastic search (MOSS), that extends these techniques and proves superior at quickly finding graphical models with high posterior probability. We then develop a novel stochastic search technique for multivariate regression models and conclude with a real-world example from the recent covariance estimation literature. Supplemental materials are available online.

Economic emission dispatch of thermal generating units using genetic algorithm technique

Well-established conventional algorithms are available for solving the economic emission dispatch (EED) problem. But the recent trend is to use the tools such as genetic algorithms (GAs) evolutionary programming technique, etc., because of some of their superior qualities. In this paper, GA technique has been applied for solving EED problem. This GA technique represents a class of general purpose stochastic search techniques which simulate natural inheritance by genetics illustrated by considering a five-bus system.

Security Constrained Unit Commitment by a new adaptive hybrid stochastic search technique

In this paper, a new formulation of Security Constrained Unit Commitment (SCUC) problem, considering more practical constraints and nonlinear characteristics than previous works in the area, is presented. The proposed SCUC formulation includes prohibited operating zones (POZs), valve-loading effects, and multiple fuel options of generating units, in addition to prevailing constraints, such as hourly power demand, system reserves, ramp up/down limits, minimum ON/OFF duration limits, fuel constraints and environmental limits. Unlike the traditional SCUC techniques that solve the economic dispatch (ED) first and then check it with the network for any violation, the proposed method solves the Security Constrained Economic Dispatch (SCED) from the UC. To solve such SCUC model, a hybrid solution method consists of an adaptive binary particle swarm optimization (ABPSO) and an Adaptive Real Coded Genetic Algorithm (ARCGA) is proposed. The effectiveness of the suggested method to solve the SCUC problem is shown on different test systems.

An enhanced genetic algorithm with simulated annealing for job-shop scheduling

The Job-Shop Scheduling Problem (JSSP) is one of the most difficult problems, as it is classified as NP-Hard problem. The main objective of the JSSP is to find a schedule of operations that can minimize the maximum completion time (called makespan) that is the completed time of carrying total operations out in the schedule for n jobs and m machines. In many cases, the combination of goals and resources exponentially increases the search space, and thus the generation of consistently good scheduling is particularly difficult, because we have a very large combinatorial search space and precedence constraints between operations. Exact methods such as the branch and bound method and dynamic programming take considerable computing time to obtain the optimum solution. In order to overcome this difficulty, it is more sensible to obtain a good solution near the optimal one. Stochastic search techniques such as evolutionary algorithms can be used to find a good solution. In this paper we proposed a new method for solving job-shop scheduling problem using hybrid Genetic Algorithm (GA) with Simulated Annealing (SA). This method introduces a reasonable combination of local search and global search for solving JSSP. Keywords: Job-shop scheduling, genetic algorithm, simulated annealing, local search, global search

XtalOpt: An open-source evolutionary algorithm for crystal structure prediction

The implementation and testing of XtalOpt, an evolutionary algorithm for crystal structure prediction, is outlined. We present our new periodic displacement (ripple) operator which is ideally suited to extended systems. It is demonstrated that hybrid operators, which combine two pure operators, reduce the number of duplicate structures in the search. This allows for better exploration of the potential energy surface of the system in question, while simultaneously zooming in on the most promising regions. A continuous workflow, which makes better use of computational resources as compared to traditional generation based algorithms, is employed. Various parameters in XtalOpt are optimized using a novel benchmarking scheme. XtalOpt is available under the GNU Public License, has been interfaced with various codes commonly used to study extended systems, and has an easy to use, intuitive graphical interface. Program summary Program title: XtalOpt Catalogue identifier: AEGX_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEGX_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GPL v2.1 or later [1] No. of lines in distributed program, including test data, etc.: 36 849 No. of bytes in distributed program, including test data, etc.: 1 149 399 Distribution format: tar.gz Programming language: C++ Computer: PCs, workstations, or clusters Operating system: Linux Classification: 7.7 External routines: QT [2], OpenBabel [3], AVOGADRO [4], SPGLIB [8] and one of: VASP [5], PWSCF [6], GULP [7]. Nature of problem: Predicting the crystal structure of a system from its stoichiometry alone remains a grand challenge in computational materials science, chemistry, and physics. Solution method: Evolutionary algorithms are stochastic search techniques which use concepts from biological evolution in order to locate the global minimum on their potential energy surface. Our evolutionary algorithm, XtalOpt, is freely available to the scientific community for use and collaboration under the GNU Public License. Running time: User dependent. The program runs until stopped by the user.

Partial transmit sequences for PAPR reduction of OFDM signals with stochastic optimization techniques

This paper considers the use of the partial transmit sequence (PTS) technique in reducing the peak-to-average power ratio (PAPR) of an orthogonal frequency division multiplexing (OFDM) signal. The conventional PTS technique is highly successful in PAPR reduction for OFDM signals, but the considerable computational complexity for the required search through a high-dimensional vector space is a potential problem for practical implementation. To reduce the search complexity while still improving the PAPR statistics, stochastic optimization techniques such as the simulated annealing (SA) algorithm, Cross-Entropy (CE) method, and particle swarm optimization (PSO) have recently been proposed to search for a phase factor that reduces both the PAPR statistics and the computational load. In this paper, a novel stochastic optimization approach, that is, the electromagnetism-like (EM) algorithm, is applied to reduce the PAPR of an OFDM signal. The computer simulation results show that compared with the various stochastic search techniques developed previously, the proposed EM method obtains the most desirable PAPR reduction with low computational complexity.

Self-adaptive learning based particle swarm optimization

Particle swarm optimization (PSO) is a population-based stochastic search technique for solving optimization problems over continuous space, which has been proven to be efficient and effective in wide applications in scientific and engineering domains. However, the universality of current PSO variants, i.e., their ability to achieve good performance on a variety of different fitness landscapes, is still unsatisfying. For many practical problems, where the fitness landscapes are usually unknown, employing a trial-and-error scheme to search for the most suitable PSO variant is computationally expensive. Therefore, it is necessary to develop a more adaptive and robust PSO version to provide users a black-box tool for various application problems. In this paper, we propose a self-adaptive learning based PSO (SLPSO) to make up the above demerits. SLPSO simultaneously adopts four PSO based search strategies. A probability model is used to describe the probability of a strategy being used to update a particle. The model is self-adaptively improved according to the strategies’ ability of generating better quality solutions in the past generations. In order to evaluate the performance of SLPSO, we compare it with eight state-of-the-art PSO variants on 26 numerical optimization problems with different characteristics such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise. The experimental results clearly verify the advantages of SLPSO. Moreover, a practical engineering problem, the economic load dispatch problem of power systems (ELD), is used to further evaluate SLPSO. Compared with the previous effective ELD evolutionary algorithms, SLPSO can update the best solution records.

Particle swarm optimization applied to solving and appraising the streaming-potential inverse problem

Water flow in the subsoil generates electrical currents measurable at the ground surface with the self-potential (SP) method. These measured potentials, which result from hydroelectric coupling, are called streaming potentials and are well correlated with the geometry of the water table. The particle swarm algorithm can be used to estimate the water-table elevation from SP data measured at the ground surface. The basic idea behind particle swarm optimization (PSO) is that each model searches the model space according to its misfit history and the misfit of the other models (particles) of the swarm. PSO is a simple, robust, and versatile algorithm with a very good convergence rate (typically before 3000 forward runs), and it can explore a large model space without being time consuming. Based on samples gathered in a low-misfit area, we have computed a fast approximation of the posterior distribution of the water table, the electrokinetic coupling constant, and the reference hydraulic head. Although PSO is a stochastic search technique, our convergence results, based on the stability of particle trajectories, specify clear criteria to tune PSO parameters.

Multi-objective differential evolution with diversity enhancement

Multi-objective differential evolution (MODE) is a powerful and efficient population-based stochastic search technique for solving multi-objective optimization problems in many scientific and engineering fields. However, premature convergence is the major drawback of MODE, especially when there are numerous local Pareto optimal solutions. To overcome this problem, we propose a MODE with a diversity enhancement (MODE-DE) mechanism to prevent the algorithm becoming trapped in a locally optimal Pareto front. The proposed algorithm combines the current population with a number of randomly generated parameter vectors to increase the diversity of the differential vectors and thereby the diversity of the newly generated offspring. The performance of the MODE-DE algorithm was evaluated on a set of 19 benchmark problem codes available from http://www3.ntu.edu.sg/home/epnsugan. With the proposed method, the performances were either better than or equal to those of the MODE without the diversity enhancement.

Application of Genetic Algorithms to Solve the Multidepot Vehicle Routing Problem

This paper deals with the optimization of vehicle routing problem in which multiple depots, multiple customers, and multiple products are considered. Since the total traveling time is not always restrictive as a time window constraint, the objective regarded in this paper comprises not only the cost due to the total traveling distance, but also the cost due to the total traveling time. We propose to use a stochastic search technique called fuzzy logic guided genetic algorithms (FLGA) to solve the problem. The role of fuzzy logic is to dynamically adjust the crossover rate and mutation rate after ten consecutive generations. In order to demonstrate the effectiveness of FLGA, a number of benchmark problems are used to examine its search performance. Also, several search methods, branch and bound, standard GA (i.e., without the guide of fuzzy logic), simulated annealing, and tabu search, are adopted to compare with FLGA in randomly generated data sets. Simulation results show that FLGA outperforms other search methods in all of three various scenarios.