Deterministic Search Method Research Articles

Energetics and spectroscopic studies of clusters and the temperature dependencies of the isomers: An approach based on a combined recipe of parallel tempering and quantum chemical methods.

A system associated with several number of weak interactions supports numerous number of stable structures within a narrow range of energy. Often, a deterministic search method fails to locate the global minimum geometry as well as important local minimum isomers for such systems. Therefore, in this work, the stochastic search technique, namely parallel tempering, has been executed on the quantum chemical surface of the system for -8 to generate global minimum as well as several number of local minimum isomers. IR spectrum can act as the fingerprint property for such system to be identified. Thus, IR spectroscopic features have also been included in this work. Vertical detachment energy has also been calculated to obtain clear information about number of water molecules in several spheres around the central anion. In addition, in a real experimental scenario, not only the global but also the local minimum isomers play an important role in determining the average value of a particular physically observable property. Therefore, the relative conformational populations have been determined for all the evaluated structures for the temperature range between 20K and 400K. Further to understand the phase change behavior, the configurational heat capacities have also been calculated for different sizes.

The use of coevolutionary algorithms for optimizing the operating regimes of the roasting conveyor machine

In modern conditions of constant growth in prices for fuel and energy resources, the problem of increasing the energy and resource efficiency of technological processes of industrial enterprises has acquired particular relevance. It is especially acute for energy-intensive industries, which include high-temperature processing of mining and chemical raw materials. To reduce the energy intensity of complex chemical-technological processes, it is proposed to use the possibilities of computer simulation, for example, to optimize the operating regimes of existing equipment. The article has considered the scientific and practical problem of optimizing the charge heating regimes in various zones of the roasting conveyor machine used to produce phosphorite pellets from apatite-nepheline ore waste stored in dumps of mining and processing plants. The specifics of the optimization task (nonlinearity of the objective function, large dimension of the search space, high computational complexity) are significant limitations for the use of traditional deterministic search methods. It led to the choice of population algorithms, which are based on modeling the collective behavior and are distinguished by the possibility of simultaneous processing of several options. The cuckoo search algorithm, which is distinguished by a small number of “free” parameters that affect the convergence, was used to solve the stated optimization task. To select the optimal values of these parameters, it was proposed to use the idea of coevolution, which consists in the parallel launch of several versions of the selected algorithm with different “settings” for each subpopulation. The management of the chemical-technological system for the processing of apatite-nepheline ore waste, taking into account the basis of the results obtained, will minimize the amount of return and ensure an energy-saving operating regime of the roasting conveyor machine.

Using population algorithms to optimize the temperature regime of roasting phosphorite pellets

The problem of rational energy resource use is especially acute for energy- intensive industries, which include high-temperature processing of mining chemical raw materials (for example, the production of phosphorite pellets from apatite-nepheline ore waste by drying and roasting). In this regard, the temperature modes of roasting conveyor machine should ensure not only the completion of the ongoing chemical-technological processes and the required product quality, but also energy and resource saving. Thus, there is an urgent scientific and practical task of optimizing charge heating modes based on the results of modeling heat and mass transfer processes occurring in various zones of the roasting conveyor machine. The impossibility of carrying out expensive full-scale experiments leads to the need to use computer simulation methods. Nonlinearity, large dimension of the search space, high computational complexity make it difficult to use traditional deterministic search methods. Under these conditions, the stochastic methods that deliberately introduce an element of randomness into the search algorithm show good results. Today, population algorithms based on modeling the collective behavior of living organisms and characterized by the ability to simultaneously process several options have become widespread. To solve the optimization problem, it is proposed to use a modified Cuckoo search algorithm (by introducing fuzzy elements), which provides a comprehensive account of a huge number of parameters set for each vacuum chamber of the roasting conveyor machine. The control of the chemical-energy-technological system for the processing of apatite-nepheline ores waste, taking into account the obtained data and based on the existing neural network model of the high-temperature process, will make it possible to minimize the amount of return and provide energy-saving conditions for the operation of roasting units.

Boosting the Cellular Network Coverage Optimization in Accordance With the Metric Structure of Antenna Variables

Cellular networks are bound to connect an ever-increasing number of subscribers. The issue of securing both sufficient capacity and reliable coverage remains to be resolved. This paper introduces the maximum coverage problem in wireless cellular networks and gets insight into the metric structure of the solution space for antenna orientation variables. We construct two metric spaces in mathematical views, in which both the deterministic search method (e.g., Nelder-Mead simplex algorithm) and the stochastic search method (e.g., Genetic Algorithm) have been fully discussed without using gradient information. Accordingly, we propose the improved deterministic and stochastic search methods to boost the coverage optimization procedure. Experiments show that the proposed algorithms not only obtain the close-to-optimal solution but greatly improve the convergence speed by reason that redundant exploration is avoided in the tailored solution spaces. Metric structure, as an essential topology in the antenna orientation solution space, therefore, provides a new perspective to settle other antenna orientation-related coverage and capacity optimization problems.

Minimising Non-Processing Energy Consumption and Tardiness Fines in a Mixed-Flow Shop

To meet the increasingly diversified demand of customers, more mixed-flow shops are employed. The flexibility of mixed-flow shops increases the difficulty of scheduling. In this paper, a mixed-flow shop scheduling approach (MFSS) is proposed to minimise the energy consumption and tardiness fine (TF) of production with a special focus on non-processing energy (NPE) reduction. The proposed approach consists of two parts: firstly, a mathematic model is developed to describe how NPE and TF can be determined with a specific schedule; then, a multi-objective evolutionary algorithm with multi-chromosomes (MCEAs) is developed to obtain the optimal solutions considering the NPE-TF trade-offs. A deterministic search method with boundary (DSB) and a non-dominated sorting genetic algorithm (NSGA) are employed to validate the developed MCEA. Finally, a case study on an extrusion die mixed-flow shop is performed to demonstrate the proposed approach in industrial practice. Compared with three traditional scheduling approaches, the better performance of the MFSS in terms of computational time and solution quality could be demonstrated.

Deterministic Search Methods for Computational Protein Design.

One main challenge in Computational Protein Design (CPD) lies in the exploration of the amino-acid sequence space, while considering, to some extent, side chain flexibility. The exorbitant size of the search space urges for the development of efficient exact deterministic search methods enabling identification of low-energy sequence-conformation models, corresponding either to the global minimum energy conformation (GMEC) or an ensemble of guaranteed near-optimal solutions. In contrast to stochastic local search methods that are not guaranteed to find the GMEC, exact deterministic approaches always identify the GMEC and prove its optimality in finite but exponential worst-case time. After a brief overview on these two classes of methods, we discuss the grounds and merits of four deterministic methods that have been applied to solve CPD problems. These approaches are based either on the Dead-End-Elimination theorem combined with A* algorithm (DEE/A*), on Cost Function Networks algorithms (CFN), on Integer Linear Programming solvers (ILP) or on Markov Random Fields solvers (MRF). The way two of these methods (DEE/A* and CFN) can be used in practice to identify low-energy sequence-conformation models starting from a pairwise decomposed energy matrix is detailed in this review.

Biologically Inspired Node Generation Algorithm for Path Planning of Hyper-redundant Manipulators Using Probabilistic Roadmap

This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration space surrounding existing nodes in the roadmap and uses a combination of random and deterministic search methods that emulate the behaviour of octopus limbs. The strategy consists of randomly mutating the states of the links near the end-effector, and mutating the states of the links near the base of the robot toward the states of the goal configuration. When combined with the small tree probabilistic roadmap planner, the method was successfully used to solve the narrow passage motion planning problem of a 17 degree-of-freedom manipulator.

Better Implementation of Evolutionary Algorithm through Mutant Function

Evolutionary algorithm is a probabilistic counterpart to a deterministic search method that impersonates the representation of natural biological evolution. Evolutionary algorithm (EA) operates on a population of potential solutions applying the principle of survival of the fittest to produce better and better estimates to a solution. At each generation, a new set of guesses is created by the process of selecting individuals according to their level of fitness in the problem domain and upbringing them together using operators copied from natural genetics. Evolutionary programming is similar to genetic programming, but the structure of the program is secure and its numerical parameters are allowed to change. The concept leads to the evolution of populations of individuals that are better suited to their environment than the individuals that they were created from, just as in natural adaptation. Mutability means for objects which can be changed and a mutant function the object. In this paper we target a string and an array of random characters chosen from the set of upper-case alphabets together with the space, and of the same length as the target string. A fitness function computes the 'closeness' of its argument to the target string. A mutant function with a string and a mutation rate returns a copy of the string, with some characters mutated. Finally after several iteration it mutates to target string successfully.

Tree search algorithm for assigning cooperating UAVs to multiple tasks

AbstractThis paper describes a tree search algorithm for assigning cooperating homogeneous uninhabited aerial vehicles to multiple tasks. The combinatorial optimization problem is posed in the form of a decision tree, the structure of which enforces the required group coordination and precedence for cooperatively performing the multiple tasks. For path planning, a Dubin's car model is used so that the vehicles' constraint, of minimum turning radius, is taken into account. Due to the prohibitive computational complexity of the problem, exhaustive enumeration of all the assignments encoded in the tree is not feasible. The proposed optimization algorithm is initialized by a best‐first search and candidate optimal solutions serve as a monotonically decreasing upper bound for the assignment cost. Euclidean distances are used for estimating the path length encoded in branches of the tree that have not yet been evaluated by the computationally intensive Dubin's optimization subroutine. This provides a lower bound for the cost of unevaluated assignments. We apply these upper and lower bounding procedures iteratively on active subsets within the feasible set, enabling efficient pruning of the solution tree. Using Monte Carlo simulations, the performance of the search algorithm is analyzed for two different cost functions and different limits on the vehicles' minimum turn radius. It is shown that the selection of the cost function and the limit have a considerable effect on the level of cooperation between the vehicles. The proposed deterministic search method can be applied on line to different sized problems. For small‐sized problems, it provides the optimal solution. For large‐sized problems, it provides an immediate feasible solution that improves over the algorithm's run time. When the proposed method is applied off line, it can be used to obtain the optimal solution, which can be used to evaluate the performance of other sub‐optimal search methods. Copyright © 2007 John Wiley & Sons, Ltd.

Functional Test Generation Based on Combined Random and Deterministic Search Methods

The aim of this paper is to explore some features of the functional test generation problem, and on the basis of the gained experience, to propose a practical method for functional test generation. In the paper presented analysis of random search methods and adjacent stimuli generation allowed formulating a practical method for generating functional tests. This method incorporates the analyzed termination conditions of generation, exploits the advantages of random and deterministic search, as well as the feature that the sets of the selected input stimuli can be merged easily in order to obtain a better set of test patterns.

A Hybrid Genetic Algorithm for Mixed-Discrete Design Optimization

A new hybrid genetic algorithm is presented for the solution of mixed-discrete nonlinear design optimization. In this approach, the genetic algorithm (GA) is used mainly to determine the optimal feasible region that contains the global optimum point, and the hybrid negative subgradient method integrated with discrete one-dimensional search is subsequently used to replace the GA to find the final optimum solution. The hybrid genetic algorithm, combining the advantages of random search and deterministic search methods, can improve the convergence speed and computational efficiency compared with some other GAs or random search methods. Several practical examples of mechanical design are tested using the computer program developed. The numerical results demonstrate the effectiveness and robustness of the proposed approach.

IDENTIFYING SOFTWARE COMPONENT ASSOCIATION WITH GENETIC ALGORITHM

Identifying software component association is useful for component management and component retrieval. In this paper we design an evolutionary strategy to understand software structure better and identify software component association, by using genetic algorithm. Our mining strategy is effective for global search, especially when the searched space is so large that it is hardly possible to use deterministic search method.

A comparison of neighborhood search techniques for multi-objective combinatorial problems

In this paper we compare the performance of simulated annealing and tabu search when both are applied to a large, complex multi-objective flow-shop problem. Repeated descent, an older neighborhood search technique, is used as a basis for comparison. The best parameters needed for the methods and their subsequent performance are compared on two further levels: as the total number of perturbations allowed increases and as the number of objectives increases. It is shown that, as the number of objectives (and thus the complexity of the problem) increases, simulated annealing (which epitomizes randomized search methods) becomes more attractive than tabu search (which epitomizes deterministic search methods). In addition, the relative value of simulated annealing is greater when only a small number of perturbations are allowed. Reasons are offered for these phenomena. These experiments identify the need for appropriate measures of complexity of a combinatorial problem. Two such measures are described, and it is shown that these can lead us to revise our a priori notions about the complexity of multi-objective combinatorial problems. In particular, the complexity of a combinatorial problem may be strongly influenced by the type of objectives as well as their number.

Preliminary orbit determination by a composite multivariate search strategy

A new strategy is presented for the problem of preliminary orbit determination with limited data and a very approximate initial estimate. This strategy, which is based on the minimization of the observation error func- tion, is essentially a combination of two types of multivariate search, random and deterministic, in the parameter space, ordered in such a way as to exploit the relative inherent advantages of the two types of methods. The feasibility of achieving accurate state estimates with such a strategy has been explored by simula- tion studies. Further, the potential practical applicability of the proposed method has been demonstrated using a variety of actual tracking data sets and orbit conditions. EVERAL methods have been reported in the literature1'2 for the orbital state estimation of a satellite using noisy ground observation data such as range, range rate, azimuth and elevation angles, of which the differential correction method stands out as the most widely used. However, most of these approaches have an inherent disadvantage in that a close a priori state estimate is required to converge to the correct state rapidly. But such needs as construction of the orbital state quickly with the knowledge of only the prelaunch in- jection parameters, augmented by several minutes of single station tracking data, estimation of the state immediately after orbit correction maneuvers are encountered in practice. In order to handle such scenarios wherein the conventional methods may npt always be able to accomplish the objective, several geometric methods have been devised which make use of certain combinations of observation types to give an ap- proximate state estimate. Gauss's m^ Gibb's method and the Double v-iter^tion are some of the widely used methods of this kind.3'4 Though variations of Gibb's method have also been proposed in the literature that use specific observation types such as range alone, there is definitely a dearth for universal methods for preliminary orbit deter- mination which can use any one or a combination of ob- servation types. The optimization approach to minimize the weighted sum of the residuals is an obvious avenue that could be explored towards this end. In Ref. 5 there are reported the findings of a comparative study of applying several op- timization techniques to the problem of orbit determination, using simulated data comprising more th^n one observation type and spread over two or three visible passes over a tracking station. In view of the existence of local minima, direct application of the deterministic search methods may lead to convergence to wrong states as can be seen in the results of Ref. 5. Such a problem, which makes preliminary orbit determination by direct search an unattractive ap- proach, can be alleviated to a large extent by a suitable combination of both random and deterministic searches in the parameter space. In this paper such a strategy is proposed and its feasibility demonstrated by simulation exercises and real- world case studies.