Combinatorial Optimization Algorithm Research Articles

An Efficient Path Planning Algorithm for Networked Robots using Modified Optimization Algorithm

Path planning has played a significant role in major numerous decision-making techniques through an automatic system involved in numerous military applications. In the last century, pathfinding and generation were carried out by multiple intelligent approaches. It is very difficult in pathfinding to reduce energy. Besides suggesting the shortest path, it has been found that optimal path planning. This paper introduces an efficient path planning algorithm for networked robots using modified optimization algorithms in combination with the η3 -splines. A new method has employed a cuckoo optimization algorithm to handle the mobile robot path planning problem. At first, η3 - splines are combined so an irregular set of points can be included alongside the kinematic parameters chosen to relate with the development and the control of mobile robots. The proposed algorithm comprises of adaptive random fluctuations (ARFs), which help to deal with the very much manageable neighborhood convergence. This algorithm carries out the process of accurate object identification along with analyzing the influence of different design choice by developing a 3D CNN architecture to determine its performance. Besides offering classification in real-time applications, the proposed algorithm outperforms the performance of state of the art in different benchmarks.

Compensation for target speckle phase by use of the combination of the adaptive particle swarm optimization algorithm and the array detector method in heterodyne detection

This study presents a robust extension of a widely used array detector method for overcoming the speckle effect in active coherent detection. We consider the heterodyne frequency component of the total output of the array detector as the fitness function. The adaptive particle swarm optimization (APSO) algorithm is introduced into the array detector method to calculate the quantities of phase adjustment of each array element. Based on the numerical results, the proposed array detector model with APSO algorithm exhibits more efficient performance in compensation for the spatial speckle phase in optical coherent detection compared with other commonly used eight combinatorial optimization algorithms.

Novel high pressure structures and mechanical properties of rhenium diboride

The pressure-induced phase transition and mechanical properties of rhenium diboride (ReB2) have been investigated by using the particle swarm optimization algorithm in combination with density functional theory calculations. It was found that the P63/mmc structure of ReB2 (hP6-ReB2) is the most stable phase at ambient conditions, and it will transform to P6/mmm structure (hP3-ReB2) at about 300.7 GPa. Phonon dispersion curve calculations suggest that hP6-ReB2 and hP3-ReB2 possess dynamical stabilities in a wide range of 0–400 and 120–400 GPa, respectively. Moreover, the calculated hardness of hP6-ReB2 is 38.1 GPa at ambient conditions, suggesting it is a potential hard material. However, hP3-ReB2 just possesses the hardness value of 18.9 GPa at the pressure of 120 GPa. Finally, by means of the quasiharmonic approximation method, the high-pressure and finite-temperature phase diagram of ReB2 is proposed. It is found that the transition pressure of hP6-ReB2 to hP3-ReB2 decreases with increasing temperatures.

Interactive Ant Colony Optimization to Support Adaptation in Serious Games

The success of serious games usually depends on their capabilities to engage learners and to provide them with personalized gaming and learning experiences. Therefore, it is important to equip a game, as an autonomous computer system, with a certain level of understanding about individual learning trajectories and gaming processes. AI and machine learning technologies increasingly enter the field; these technologies often fail, however, since serious games either pose highly complex problems (combining gaming and learning process) or do not provide the extensive data bases that would be required. An interesting new direction is augmenting the strength of AI technologies with human intuition and human cognition. In the present paper, we investigated performance of the MAXMIN Ant System, a combinatorial optimization algorithm, with and without human interventions to the algorithmic procedure. As a testbed, we used a clone of the Travelling Salesman problem, the Travelling Snakesman game. We found some evidence that human interventions result in superior performance than the algorithm alone. The results are discussed regarding the applicability of this pathfinding algorithm in adaptive games, exemplified by Micro Learning Space adaptation systems.

T-Tree: The Tokyo toolbox for large-scale combinatorial auction experiments

This paper provides a brief introduction to t-Tree, a general auction software package that implements single-unit auctions, Vickrey–Clarke–Groves auctions (“VCG auctions”), Simultaneous Multiple Round Auctions (“SMRA”), and Combinatorial Clock Auctions (“CCA”) used in transportation, logistics, procurements, and spectrum allocation. The critical contribution of t-Tree is that it can handle computational complexities of the winner determination problem in large-scale combinatorial auctions by natively implementing Zhong et al. (2004)’s combinatorial optimization algorithms. Thus researchers can study large-scale combinatorial auction mechanisms off the shelf using t-Tree.

Parameter Matching and Optimization of a Hybrid Type DC SFCL Considering the Transient Characteristics of VSC-Based DC Systems

Voltage source converter-based (VSC-based) DC systems play an important role in connecting large-scale renewable energy and distributed energy, but they are vulnerable to DC short-circuit fault and lacks mature protection devices and appropriate protection strategies. Therefore, a hybrid type DC superconducting fault current limiter (H-SFCL) is proposed and the current limiting mechanism of the SFCL is analyzed. According to the requirements and strategies for protection, several different effective parameter matching and optimization methods of the H-SFCL are proposed by combining optimization algorithms and two short-circuit transient calculation models of VSC-based DC systems. The optimization methods proposed in this paper are compared and analyzed in terms of convergence, running time, calculation range and stability of optimization results, revealing their respective calculation characteristics. Finally, the effectiveness of parameter matching and optimization methods are well validated by comparison and analysis of simulation. The proposed methods can select a good parameter matching scheme of the H-SFCL to deal with different requirements.

Graph coloring: a novel heuristic based on trailing path—properties, perspective and applications in structured networks

Graph coloring is a manifestation of graph partitioning, wherein a graph is partitioned based on the adjacency of its elements. The fact that there is no general efficient solution to this problem that may work unequivocally for all graphs opens up the realistic scope for combinatorial optimization algorithms to be invoked. The algorithmic complexity of graph coloring is non-deterministic in polynomial time and hard. To the best of our knowledge, there is no algorithm as yet that procures an exact solution of the chromatic number comprehensively for any and all graphs within the polynomial (P) time domain. Here, we present a novel heuristic, namely the ‘trailing path’, which returns an approximate solution of the chromatic number within P time, and with a better accuracy than most existing algorithms. The ‘trailing path’ algorithm is effectively a subtle combination of the search patterns of two existing heuristics (DSATUR and largest first) and operates along a trailing path of consecutively connected nodes (and thereby effectively maps to the problem of finding spanning tree(s) of the graph) during the entire course of coloring, where essentially lies both the novelty and the apt of the current approach. The study also suggests that the judicious implementation of randomness is one of the keys toward rendering an improved accuracy in such combinatorial optimization algorithms. Apart from the algorithmic attributes, essential properties of graph partitioning in random and different structured networks have also been surveyed, followed by a comparative study. The study reveals the remarkable stability and absorptive property of chromatic number across a wide array of graphs. Finally, a case study is presented to demonstrate the potential use of graph coloring in protein design—yet another hard problem in structural and evolutionary biology.

An Ensemble Learning Imbalanced Data Classification Method Based on Sample Combination Optimization

Imbalanced data classification is one of the hot topics in data mining and machine learning in recent years. In practice, Imbalanced data classification is very common, such as cancer detection, spam discrimination, credit card fraud detection, etc. Because of the large difference in the number of categories and Imbalanced distribution, traditional classification algorithms have poor classification effect on minority classes, and correct identification of minority classes often brings greater value. Therefore, how to effectively identify minority classes in Imbalanced data is of great importance. Practical significance. Aiming at the problems that the Bagging-based Imbalanced data classification method cannot guarantee the validity and existence of classification boundaries by adding redundant noise information and sampling, an ensemble learning GABagging method based on sample combination optimization is proposed. Firstly, the sample combination optimization algorithm uses genetic algorithm to select a subset from most classes and construct a new data set with a few classes. Subsequently, several sample combinatorial optimization algorithms are used to train and integrate several classifiers. The experimental results show that GABagging can improve the correct recognition ability of minority classes on 19 Imbalanced datasets compared with other similar methods such as TPR and AUC, without excessive loss of recognition ability of majority classes. It is proved that GABagging can compensate for the shortcomings of related Bagging-based methods such as easy loss, increasing samples and not guaranteeing the validity and existence of classification boundaries after sampling.

Active Preference Learning Based on Generalized Gini Functions: Application to the Multiagent Knapsack Problem

We consider the problem of actively eliciting preferences from a Decision Maker supervising a collective decision process in the context of fair multiagent combinatorial optimization. Individual preferences are supposed to be known and represented by linear utility functions defined on a combinatorial domain and the social utility is defined as a generalized Gini Social evaluation Function (GSF) for the sake of fairness. The GSF is a non-linear aggregation function parameterized by weighting coefficients which allow a fine control of the equity requirement in the aggregation of individual utilities. The paper focuses on the elicitation of these weights by active learning in the context of the fair multiagent knapsack problem. We introduce and compare several incremental decision procedures interleaving an adaptive preference elicitation procedure with a combinatorial optimization algorithm to determine a GSF-optimal solution. We establish an upper bound on the number of queries and provide numerical tests to show the efficiency of the proposed approach.

Weighted superposition attraction algorithm for combinatorial optimization

Weighted Superposition Attraction (WSA) is a swarm based metaheuristic algorithm that is lately proposed for solving continuous optimization problems. Performance of the WSA was comprehensively tested with numerous unconstrained/constrained optimization problems. Due to its reasonable performance on continuous optimization problems we were motivated to develop a combinatorial version of WSA. A new method is devised to handle combinatorial problems without requiring an indirect representation mechanism like random keys. A random walk procedure is integrated into the combinatoric WSA (cWSA) so as to improve its diversification capability. Additionally, opposition based learning is also integrated into cWSA to increase its performance further. Performance of cWSA is tested on two well-known combinatorial optimization problems, namely the Resource Constrained Project Scheduling Problem (RCPSP) and the Permutation Flow Shop Scheduling Problem (PFSP). The results of the extensive computational study point out the efficiency of the proposed cWSA.

Design, construction and monitoring of an ice composite shell structure

Ice structures are widely constructed in cold regions as landscapes or shelters. However, owing to the poor mechanical properties of the material and the masonry construction method, the appearance modelling and building scale of ice structures are limited. It is necessary to discover an innovative high-performance ice material and to establish a large-span ice shell construction method. Based on previous studies, a free-form ice composite shell is designed and constructed with an inflatable formwork. A combined optimization algorithm is proposed to determine a reasonable inflatable formwork for the construction, and exhaustive structural investigations on the behaviour of the ice composite shell under multiple load conditions are conducted. Subsequently, some key technologies for the construction process and health monitoring of the ice composite shell are discussed. The results show that the ice composite material is suitable for the construction of large-span ice shells. The morphological design objectives of the free-form ice shell are achieved based on the previously mentioned optimization algorithm, and the structural analysis provides a theoretical foundation for the structural safety and construction quality of the ice shell. Moreover, the effects of the ambient temperature and solar radiation are not negligible, and further research is required. • The whole process of one ice composite shell built in cold region is discussed. • The 1% cellulose–water mixture is chosen as the structural analysis and construction pumping material. • Form-finding of the inflatable formwork is practiced combining with two optimization algorithms. • A series of works on structural monitoring are conducted. • The temperature and sunshine radiation have a great influence on the ice shell.

OPTIMIZATION OF DESIGN PARAMETERS OF AUTOTRANSFORMERS IN ICE MELTING SCHEME WITH NON-INDUCTIVE CIRCUIT ON 6-10 KV OVERHEAD POWER LINES

Purpose. The purpose of the paper is to determine the basic electrical characteristics and to develop a calculation method and algorithm for optimizing the design parameters of autotransformers intended for use in a melting ice scheme with a non-inductive circuit on 6-10 kV overhead power lines. Methodology. The development of the technical and economic model and the method for calculation of the design parameters of the autotransformer for melting ice is performed on the basis of a systematic approach. Optimization of structural characteristics of autotransformers is carried out using a combined algorithm based on the spatial grid method, adapted to the case of a mixed space of discrete and continuous independent variables, and the specifics of the technical and economic model of the autotransformer. The proposed combined optimization algorithm is implemented in the Delphi environment. Results. Based on the required specific melting power, the main electrical characteristics of autotransformers intended for use in the melting ice scheme with a non-inductive circuit on 6-10 kV overhead lines, which were the basis for optimizing their design parameters, have been calculated. The technical and economical model of autotransformer for melting ice, which is defined by nine independent variables and describes its cost and technical parameters, is developed. On the basis of the obtained electrical characteristics, optimization of the design parameters of a series of autotransformers is carried out, which includes three standard sizes, differing in maximum length of the transmission line. Originality. A method of calculation of structural parameters of autotransformers for ice melting is proposed, the peculiarity of which is the use of the criterion of the minimum of the cost of the active part and taking into account the conditioned by the circuit of connection of the autotransformers the technical restrictions of errors on the value and angle of secondary current, which are important from the point of view of ensuring the permissible deviation of the specific power of melting ice. Practical value. Optimal correlations of geometrical sizes and electromagnetic loads of autotransformers for ice melting, their cost indicators, as well as the main design characteristics of the magnetic circuit and windings are established. Results of design calculation of autotransformers are sufficient for introduction of their serial production in industrial conditions.

ARBOR: A new framework for assessing the accuracy of individual tree crown delineation from remotely-sensed data

To assess the accuracy of individual tree crown (ITC) delineation techniques the same tree needs to be identified in two different datasets, for example, ground reference (GR) data and crowns delineated from LiDAR. Many studies use arbitrary metrics or simple linear-distance thresholds to match trees in different datasets without quantifying the level of agreement. For example, successful match-pairing is often claimed where two data points, representing the same tree in different datasets, are located within 5 m of one another. Such simple measures are inadequate for representing the multi-variate nature of ITC delineations and generate misleading measures of delineation accuracy. In this study, we develop a new framework for objectively quantifying the agreement between GR and remotely-sensed tree datasets: the Accuracy of Remotely-sensed Biophysical Observation and Retrieval (ARBOR) framework. Using common biophysical properties of ITC delineated trees (location, height and crown area), trees represented in different data sets were modelled as overlapping Gaussian curves to facilitate a more comprehensive assessment of the level of agreement. Extensive testing quantified the limitations of some frequently used match-pairing methods, in particular, the Hausdorff distance algorithm. We demonstrate that within the ARBOR framework, the Hungarian combinatorial optimisation algorithm improves the match between datasets, while the Jaccard similarity coefficient is effective for measuring the correspondence between the matched data populations. The ARBOR framework was applied to GR and remotely-sensed tree data from a woodland study site to demonstrate how ARBOR can identify the optimum ITC delineation technique, out of four different methods tested, based on two measures of statistical accuracy. Using ARBOR will limit further reliance on arbitrary thresholds as it provides an objective approach for quantifying accuracy in the development and application of ITC delineation algorithms.

Four phase (8/6) SRM with DTC for minimization of torque ripple

Fundamental drawback of SR (switched reluctance) motor is torque ripple which is caused because of the current switching in the phase windings and saliency. The torque ripple introduces acoustic noise and undesirable vibrations in SRM. In this paper, another solution is proposed, which can obtain direct torque control (DTC) with voltage states of minimum numbers. The converter power switches can be specifically controlled in view of the proposed space voltage vectors, and with the hysteresis-loop control of torque, the torque control can be accomplished. For SRM, the vectors with basic space voltage are transformed correspondingly to acknowledge DTC. To execute the DTC, the space voltage vectors are equivalently changed into space voltage vectors. The torque can be controlled by these developed equivalent space voltage vectors. DTC strategy has numerous points of interest, for example, simple algorithm, less torque ripple and instantaneous response to the torque command over conventional voltage control and current chopping mode control. The DTC algorithm described in this article is contrasted with ant lion optimization (ALO), non-dominating sorting search algorithm (NSGA-II) and combined moth flame optimization and genetic algorithm with recurrent neural network (CMFG-RNN) strategies.

Control Strategy for PHEB Based on Actual Driving Cycle with Driving Style Characteristic

To exert fully the energy economy performance of plug-in hybrid electric buses (PHEBs) and enhance the adaptability to different drivers and driving cycles, a control strategy for PHEB based on actual driving cycle with driving style characteristic is proposed in this paper. Through the actual city bus driving data, collected in real time, 6 actual driving cycles with driving style characteristic are fitted by using Principal Component Analysis (PCA) and Cluster Analysis (CA). Based on the 6 driving cycles, the key parameters of rule-based control strategy are optimized and established by a combinatorial optimization algorithm in Isight. Then, an identification model to recognize the current condition based on the Learning Vector Quantization (LVQ) neural network has been built and trained offline, which is integrated in the control strategy for PHEB to invoke the corresponding optimized key control parameters in real time. A hardware-in-the-loop (HIL) test is conducted, and the result shows that the proposed strategy could improve the energy consumption by 4.94%, compared with the original rule-based control strategy, and its validity and practicability are fully verified.

An active set algorithm for robust combinatorial optimization based on separation oracles

We address combinatorial optimization problems with uncertain coefficients varying over ellipsoidal uncertainty sets. The robust counterpart of such a problem can be rewritten as a second-oder cone program (SOCP) with integrality constraints. We propose a branch-and-bound algorithm where dual bounds are computed by means of an active set algorithm. The latter is applied to the Lagrangian dual of the continuous relaxation, where the feasible set of the combinatorial problem is supposed to be given by a separation oracle. The method benefits from the closed form solution of the active set subproblems and from a smart update of pseudo-inverse matrices. We present numerical experiments on randomly generated instances and on instances from different combinatorial problems, including the shortest path and the traveling salesman problem, showing that our new algorithm consistently outperforms the state-of-the art mixed-integer SOCP solver of Gurobi.

Determination of calcium and magnesium in the Solanaceae plant by near infrared spectroscopy combined with interval combination optimization algorithm

Determination of the content of inorganic nutrients was done as part of a project to improve crop yield by use of fertilizers. Near-infrared spectroscopy coupled with interval combination optimization was used to determine the inorganic nutrients of calcium and magnesium in the Solanaceae plant. Interval combination optimization was employed to choose effective intervals with the merit of assigning smaller weights to less significant variables. Compared with full spectrum partial least squares regression, interval combination optimization improved the predictive accuracy and precision for both calcium and magnesium. The result was a high predictive ability for calcium, with a root mean squared error prediction of 0.159%, and for magnesium, with a root mean squared error prediction of 0.064%. In this study, the superiority of interval combination optimization over full spectrum partial least squares was shown in the aspects of effective variables and accurate results. Near-infrared spectroscopy combined with interval combination optimization was efficient for the determination of inorganic nutrients, calcium and magnesium, in the Solanaceae plant.

Optimizing multi-robot communication under bandwidth constraints

Robots working collaboratively can share observations with others to improve team performance, but communication bandwidth is limited. Recognizing this, an agent must decide which observations to communicate to best serve the team. Accurately estimating the value of a single communication is expensive; finding an optimal combination of observations to put in the message is intractable. In this paper, we present OCBC, an algorithm for Optimizing Communication under Bandwidth Constraints. OCBC uses forward simulation to evaluate communications and applies a bandit-based combinatorial optimization algorithm to select what to include in a message. We evaluate OCBC’s performance in a simulated multi-robot navigation task. We show that OCBC achieves better task performance than a state-of-the-art method while communicating up to an order of magnitude less.

Cloud Intelligent Logistics Service Selection Based on Combinatorial Optimization Algorithm

Cloud Intelligent Logistics Service Selection Based on Combinatorial Optimization Algorithm

Study on the Resource Allocation Optimization in Cloud Computing Based on the Hybrid Optimization Algorithm

The methods of solving resource allocation are mainly heuristic algorithms which could not solve resource allocation problems in cloud computing. The hybrid optimization algorithm is studied to solve the problem. There are many different hybrid optimization algorithms. Our research hopes to find a simple and effective method. We select a combination optimization algorithm of the genetic and ant colony algorithms. In the early phase of this algorithm, with the help of the wide range search capabilities of the genetic algorithm, it finds a better solution; in the later stage of this algorithm, with the help of positive feedback and efficiency of the ant colony algorithm, it finds the optimal solution. In addition, the two algorithms convergence conditions and the way of how to make the better solution of the genetic algorithm translate into the initial pheromone distribution provisions of the ant colony algorithm are set up. At last, the algorithm was realized with a simulation environment, and a specific example was made by comparative analysis to verify the correctness and effectiveness of the algorithm