Variable Search Research Articles

Bnclassify: Learning Bayesian Network Classifiers

The bnclassify package provides state-of-the art algorithms for learning Bayesian network classifiers from data. For structure learning it provides variants of the greedy hill-climbing search, a well-known adaptation of the Chow-Liu algorithm and averaged one-dependence estimators. It provides Bayesian and maximum likelihood parameter estimation, as well as three naive-Bayes-specific methods based on discriminative score optimization and Bayesian model averaging. The implementation is efficient enough to allow for time-consuming discriminative scores on medium-sized data sets. bnclassify provides utilities for model evaluation, such as cross-validated accuracy and penalized log-likelihood scores, and analysis of the underlying networks, including network plotting via the Rgraphviz package. It is extensively tested, with over 200 automated tests that give a code coverage of 94%. Here we present the main functionalities, illustrate them with a number of data sets, and comment on related software.

Weighted sum multi-objective optimization of skew composite laminates

Optimizing composite structures to exploit their maximum potential is a realistic application with promising returns. In this research, simultaneous maximization of the fundamental frequency and frequency separation between the first two modes by optimizing the fiber angles is considered. A high-fidelity design optimization methodology is developed by combining the high-accuracy of finite element method with iterative improvement capability of metaheuristic algorithms. Three powerful nature-inspired optimization algorithms viz. a genetic algorithm (GA), a particle swarm optimization (PSO) variant and a cuckoo search (CS) variant are used. Advanced memetic features are incorporated in the PSO and CS to form their respective variants-RPSOLC (repulsive particle swarm optimization with local search and chaotic perturbation) and CHP (co-evolutionary host-parasite). A comprehensive set of benchmark solutions on several new problems are reported. Statistical tests and comprehensive assessment of the predicted results show CHP comprehensively outperforms RPSOLC and GA, while RPSOLC has a little superiority over GA. Extensive simulations show that the on repeated trials of the same experiment, CHP has very low variability. About 50% fewer variations are seen in RPSOLC as compared to GA on repeated trials.

Swarm hyperheuristic framework

Swarm intelligence is one of the central focus areas in the study of metaheuristic algorithms. The effectiveness of these algorithms towards solving difficult problems has attracted researchers and practitioners. As a result, numerous type of this algorithm have been proposed. However, there is a heavy critics that some of these algorithms lack novelty. In fact, some of these algorithms are the same in terms of the updating operators but with different mimicking scenarios and names. The performance of a metaheuristic algorithm depends on how it balance the degree of the two basic search mechanisms, namely intensification and diversification. Hence, introducing novel algorithms which contributes to a new way of search mechanism is welcome but not for a mere repetition of the same algorithm with the same or perturbed operators but different metaphor. With this regard, it is ideal to have a framework where different custom made operators are used along with existing or new operators. Hence, this paper presents a swarm hyperheuristic framework, where updating operators are taken as low level heuristics and guided by a high level hyperheuristic. Different learning approaches are also proposed to guide the intensification and diversification search behaviour of the algorithm. Hence, a swarm hyperheuristic without learning ( $${ SHH}1$$ ), with offline learning ( $${ SHH}2)$$ and with an online learning ( $${ SHH}3$$ ) is proposed and discussed. A simulation based comparison and discussion is also presented using a set of nine updating operators with selected metaheuristic algorithms based on twenty benchmark problems. The problems are selected from both unconstrained and constrained optimization problems with their dimension ranging from two to fifty. The simulation results show that the proposed approach with learning has a better performance in general.

An effective multi-wave algorithm for solving the max-mean dispersion problem

We propose an effective multi-wave algorithm organized in multiple search phases for the max-mean dispersion problem, which offers enhancement of neighborhood search algorithms by incorporating the notion of persistent attractiveness in memory based strategies. In each wave, a vertical phase and a horizontal phase are first alternated to reach a boundary solution. Then a concluding horizontal phase is executed to search around this boundary solution for further solution refinement. Finally, an oscillation phase and a diversified initial solution generation phase focus on search diversification to build well-diversified initial solutions for subsequent waves and passes. Experimental results show that the proposed approach performs quite competitive with state-of-the-art algorithms in the literature. Additional analysis discloses the benefits of the key ingredients in the proposed algorithm.

Multiple phase tabu search for bipartite boolean quadratic programming with partitioned variables

The Bipartite Boolean Quadratic Programming Problem with Partitioned Variables (BBQP-PV) is an NP-hard problem with many practical applications. In this study, we present an effective multiple phase tabu search algorithm for solving BBQP-PV. The algorithm is characterized by a joint use of three key components: two tabu search phases that employ a simple neighborhood and a very large-scale neighborhood to achieve search intensification, and a hybrid perturbation phase that adaptively chooses a greedy perturbation or a recency-based perturbation for search diversification. Experimental assessment on 50 standard benchmarks indicates that the proposed algorithm is able to obtain improved lower bounds for 5 instances and match the previously best solutions for most instances, while achieving this performance within competitive time. Additional analysis confirms the importance of the innovative search components.

Variable Neighborhood Search Methods for the Dynamic Minimum Cost Hybrid Berth Allocation Problem

This study considers the Dynamic Minimum Cost Hybrid BerthAllocation Problem (DMCHBAP) with fixed handling times of vessels.The objective function to be minimized consists of threecomponents: the costs of positioning, waiting, and tardiness ofcompletion for all vessels. Having in mind that the speed offinding high-quality solutions is of crucial importance fordesigning an efficient and reliable decision support system incontainer terminal, metaheuristic methods represent the naturalchoice to deal with DMCHBAP. Four variants of VariableNeighborhood Search (VNS) metaheuristic are designed for DMCHBAP.All four proposed VNS methods are evaluated on three classesof randomly generated instances with respect to solution quality and running times.The conducted computationalanalysis indicates that all four VNS-based methods representpromising solution approaches to DMCHBAP and similar problems inmaritime transportation. DOI: http://dx.doi.org/10.5755/j01.itc.47.3.20420

Analysing the effects of various switching probability characteristics in flower pollination algorithm for solving unconstrained function minimization problems

Due to their unique offerings, bio-inspired algorithms have become popular in problem solving. Flower pollination algorithm (FPA), which is relatively a new member of this family, is shown to be one promising algorithm and this optimizer is still open to possible enhancements. One of the reasons that adds to the popularity of FPA is indeed the simplicity in implementation. It has two basic procedures, namely global and local pollination, which correspond to global and local search, respectively. Moreover, a single parameter, referred to as switching probability, puts control on these search procedures. Thus, the mentioned switching probability actually defines the search characteristics throughout generations, which directly affects the success of FPA. Accordingly, the present work analyses the effects of various switching probability characteristics, including exponentially, linearly and sawtooth changing patterns. This is the main motivation of the present study. Secondarily, a systematically intensifying step size procedure, which is commonly ignored by most of the stochastic search algorithms, is adopted along with these strategies. The aim of the proposed step size function is to encourage a more intensified search towards the end, while providing a more diversified search at the initialization stage to avoid local optima and premature convergence. Thus, more promising results might be obtained. All developed modifications are tested by using well-known unconstrained function minimization problems. As demonstrated by several nonparametric statistical tests, results point out significant improvements over the standard FPA.

Amodal Completion of a Target Template Enhances Attentional Guidance in Visual Search.

When searching for a target object in cluttered environments, our visual system appears to complete missing parts of occluded objects—a mechanism known as “amodal completion.” This study investigated how different variants of completion influence visual search for an occluded target object. In two experiments, participants searched for a target among distractors in displays that either presented composite objects (notched shapes abutting an occluding square) or corresponding simple objects. The results showed enhanced search performance when composite objects were interpreted in terms of a globally completed whole. This search benefit for global completions was found to be dependent on the availability of a coherent, informative simple-object context. Overall, these findings suggest that attentional guidance in visual search may be based on a target “template” that represents a globally completed image of the occluded (target) object in accordance with prior experience.

Monte-Carlo Planning for Agile Legged Locomotion

Recent progress in legged locomotion research has produced robots that can perform agile blind-walking with robustness comparable to a blindfolded human. However, this walking approach has not yet been integrated with planners for high-level activities. In this paper, we take a step towards high-level task planning for these robots by studying a planar simulated biped that captures their essential dynamics. We investigate variants of Monte-Carlo Tree Search (MCTS) for selecting an appropriate blind-walking controller at each decision cycle. In particular, we consider UCT with an intelligently selected rollout policy, which is shown to be capable of guiding the biped through treacherous terrain. In addition, we develop a new MCTS variant, called Monte-Carlo Discrepancy Search (MCDS), which is shown to make more effective use of limited planning time than UCT for this domain. We demonstrate the effectiveness of these planners in both deterministic and stochastic environments across a range of algorithm parameters. In addition, we present results for using these planners to control a full-order 3D simulation of Cassie, an agile bipedal robot, through complex terrain.

Revisiting Immediate Duplicate Detection in External Memory Search

External memory search algorithms store the open and closed lists in secondary memory (e.g., hard disks) to augment limited internal memory. To minimize expensive random access in hard disks, these algorithms typically employ delayed duplicate detection (DDD), at the expense of processing more nodes than algorithms using immediate duplicate detection (IDD). Given the recent ubiquity of solid state drives (SSDs), we revisit the use of IDD in external memory search. We propose segmented compression, an improved IDD method that significantly reduces the number of false positive access into secondary memory. We show that A*-IDD, an external search variant of A* that uses segmented compression-based IDD, significantly improves upon previous open-addressing based IDD. We also show that A*-IDD can outperform DDD-based A* on some domains in domain-independent planning.

Variable Neighborhood Descent applied to Multi-way Number Partitioning Problem

This paper presents an algorithm for the optimization version of the Multi-Way Number Partitioning Problem (MWNPP). This problem consists in distributing the elements of a given sequence into k disjoint subsets so that the sums of each subset elements fit in the shortest interval. The metaheuristic Variable Neighborhood Descent (VND), a deterministic variant of Variable Neighborhood Search (VNS), adapted for solving the MWNPP, has a good performance over instances less than six subsets. It is carried out a comparative study with two algorithms, Karmarkar-Karp Heuristic and Longest Processing Time, using randomly generated instances and objective functions values. The statistical tests show that results of the VND proposed are significantly better than literature constructive methods and its improvements.

Dec-MCTS: Decentralized planning for multi-robot active perception

We propose a decentralized variant of Monte Carlo tree search (MCTS) that is suitable for a variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimize its own actions by maintaining a probability distribution over plans in the joint-action space. Robots periodically communicate a compressed form of their search trees, which are used to update the joint distribution using a distributed optimization approach inspired by variational methods. Our method admits any objective function defined over robot action sequences, assumes intermittent communication, is anytime, and is suitable for online replanning. Our algorithm features a new MCTS tree expansion policy that is designed for our planning scenario. We extend the theoretical analysis of standard MCTS to provide guarantees for convergence rates to the optimal payoff sequence. We evaluate the performance of our method for generalized team orienteering and online active object recognition using real data, and show that it compares favorably to centralized MCTS even with severely degraded communication. These examples demonstrate the suitability of our algorithm for real-world active perception with multiple robots.

Hybrid Elitist-Ant System for Nurse-Rostering Problem

The diversity and quality of high-quality and diverse-solution external memory of the hybrid Elitist-Ant System is examined in this study. The Elitist-Ant System incorporates an external memory for preserving search diversity while exploiting the solution space. Using this procedure, the effectiveness and efficiency of the search may be guaranteed which could consequently improve the performance of the algorithm and it could be well generalized across diverse problems of combinatorial optimization. The generality of this algorithm through its consistency and efficiency is tested using a Nurse-Rostering Problem. The outcomes demonstrate the competitiveness of the hybrid Elitist-Ant System’s performance within numerous datasets as opposed to those by other systems. The effectiveness of the external memory usage in search diversification is evidenced in this work. Subsequently, such usage improves the performance of the hybrid Elitist-Ant System over diverse datasets and problems.

Real-time 3DRS motion estimation for frame-rate conversion

In this article we present an updated version of a block-wise video motion estimation (ME) algorithm that is intended to be part of real-time frame rate conversion (FRC) software system. As a baseline, we choose a variant of a 3D recursive search (3DRS) algorithm, due to its relative simplicity and known real-time performance potential. We introduce updating of this algorithm, which provides effective multithreaded parallelisation in multicore systems and decreases the number of computations without a noticeable decrease in quality.

Integrated slicing tree approach for solving the facility layout problem with input and output locations based on contour distance

The designer of a plant layout faces mutually dependent subproblems that are traditionally solved in a sequential process. In this paper, a slicing tree based parallel tempering heuristic is used to solve the facility layout problem, including the location of the material handling points of each facility. To determine the input and output points on the perimeters of the departments for a given layout, two heuristic methods are presented and compared to an integer linear programming approach. The shortest paths along the perimeter of the departments are used to calculate the distance of the material flow paths from output to input points. The methods are embedded in a superordinate parallel tempering algorithm, which represents a novel approach to solving the facility layout problem. To compare the efficiency of the superordinate parallel tempering algorithm, two variants of the variable neighbourhood search are implemented as well. The effectiveness of the approach in terms of the objective function value is shown by comparing the results to those in the literature.

Harvesting and transport operations to optimise biomass supply chain and industrial biorefinery processes

In Australia, Bioenergy plays an important role in modern power systems, where many biomass resources provide greenhouse gas neutral and electricity at a variety of scales. By 2050, the Biomass energy is projected to have a 40-50 % share as an alternative source of energy. In addition to conversion of biomass, barriers and uncertainties in the production, supply may hinder biomass energy development. The sugarcane is an essential ingredient in the production of Bioenergy, across the whole spectrum ranging from the first generation to second generation, e.g., production of energy from the lignocellulosic component of the sugarcane initially regarded as waste (bagasse and cane residue). Sustainable recovery of the Lignocellulosic component of sugarcane from the field through a structured process is largely unknown and associated with high capital outlay that have stifled the growth of bioenergy sector. In this context, this paper develops a new scheduler to optimise the recovery of lignocellulosic component of sugarcane and cane, transport and harvest systems with reducing the associated costs and operational time. An Optimisation Algorithm called Limited Discrepancy Search has been adapted and integrated with the developed scheduling transport algorithms. The developed algorithms are formulated and coded by Optimization Programming Language (OPL) to obtain the optimised cane and cane residues transport schedules. Computational experiments demonstrate that high-quality solutions are obtainable for industry-scale instances. To provide insightful decisions, sensitivity analysis is conducted in terms of different scenarios and criteria.

Time–space trade-offs for Lempel–Ziv compressed indexing

Given a string S, the compressed indexing problem is to preprocess S into a compressed representation that supports fast substring queries. The goal is to use little space relative to the compressed size of S while supporting fast queries. We present a compressed index based on the Lempel–Ziv 1977 compression scheme. We obtain the following time–space trade-offs: For constant-sized alphabets(i)O(m+occlg⁡lg⁡n) time using O(zlg⁡(n/z)lg⁡lg⁡z) space, or(ii)O(m(1+lgϵ⁡zlg⁡(n/z))+occ(lg⁡lg⁡n+lgϵ⁡z)) time using O(zlg⁡(n/z)) space, For integer alphabets polynomially bounded by n(iii)O(m(1+lgϵ⁡zlg⁡(n/z))+occ(lg⁡lg⁡n+lgϵ⁡z)) time using O(z(lg⁡(n/z)+lg⁡lg⁡z)) space, or(iv)O(m+occ(lg⁡lg⁡n+lgϵ⁡z)) time using O(z(lg⁡(n/z)+lgϵ⁡z)) space, where n and m are the length of the input string and query string respectively, z is the number of phrases in the LZ77 parse of the input string, occ is the number of occurrences of the query in the input and ϵ>0 is an arbitrarily small constant. In particular, (i) improves the leading term in the query time of the previous best solution from O(mlg⁡m) to O(m) at the cost of increasing the space by a factor lg⁡lg⁡z. Alternatively, (ii) matches the previous best space bound, but has a leading term in the query time of O(m(1+lgϵ⁡zlg⁡(n/z))). However, for any polynomial compression ratio, i.e., z=O(n1−δ), for constant δ>0, this becomes O(m). Our index also supports extraction of any substring of length ℓ in O(ℓ+lg⁡(n/z)) time. Technically, our results are obtained by novel extensions and combinations of existing data structures of independent interest, including a new batched variant of weak prefix search.

Variable neighborhood search based approaches to a vehicle scheduling problem in agriculture

AbstractA vehicle scheduling problem (VSP) that arises from sugar beet transportation within minimum working time under the set of constraints reflecting a real‐life situation is considered. A mixed integer quadratically constrained programming (MIQCP) model of the considered VSP and reformulation to a mixed integer linear program (MILP) are proposed and used within the framework of Lingo 17 solver, producing optimal solutions only for small‐sized problem instances. Two variants of the variable neighborhood search (VNS) metaheuristic—basic VNS (BVNS) and skewed VNS (SVNS) are designed to efficiently deal with large‐sized problem instances. The proposed VNS approaches are evaluated and compared against Lingo 17 and each other on the set of real‐life and generated problem instances. Computational results show that both BVNS and SVNS reach all known optimal solutions on small‐sized instances and are comparable on medium‐ and large‐sized instances. In general, SVNS significantly outperforms BVNS in terms of running times.

Efficient parameter estimation of software reliability growth models using harmony search

The primary challenge of software reliability growth model is to find the unknown model parameters that are used to validate on software failure dataset. Though, numerical estimation technique plays a vital role in parameter estimation of software reliability growth models, they are not optimal as they suffer from constraints sucha as sample size, biasing, and initialisation of parameters. In this study, a parameter estimation of software reliability growth model that utilises a variant of harmony search is proposed. Extensive experiments are conducted on seven different software datasets of varying complexity. A robust experimental setup is developed employing an orthogonal array and Taguchi method. Two-fold performance comparisons are performed. First, the authors tested their proposed approach against Cuckoo search and numerical method (least square estimation) considering mean square error and Theil's statistics as a quality measure. Second, the authors applied statistical tests are performed that demonstrate the superiority of their approach over the others. The underlying motivation to conduct this study is to motivate researchers to utilise their approach for a better estimation of model parameters.

AnchorQuery: Rapid online virtual screening for small-molecule protein-protein interaction inhibitors.

AnchorQuery (http://anchorquery.csb.pitt.edu) is a web application for rational structure-based design of protein-protein interaction (PPI) inhibitors. A specialized variant of pharmacophore search is used to rapidly screen libraries consisting of more than 31 million synthesizable compounds biased by design to preferentially target PPIs. Every library compound is accessible through one-step multi-component reaction (MCR) chemistry and contains an anchor motif that is bioisosteric to an amino acid residue. The inclusion of this anchor not only biases the compounds to interact with proteins, it also enables a rapid, sublinear time pharmacophore search algorithm. AnchorQuery provides all the tools necessary for users to perform online interactive virtual screens of millions of compounds, including pharmacophore elucidation and search, and enrichment analysis. Accessibility: AnchorQuery is freely accessible at http://anchorquery.csb.pitt.edu.