Efficient Ant Colony Optimization Algorithm Research Articles

A performance efficient joint clustering and routing approach for heterogeneous wireless sensor networks

AbstractWireless sensor networks have proven to be a promising paradigm due to their wide applicability in the real‐world scenarios. Because of the resource constraint, type‐variant and battery‐powered features of the sensor nodes, the key challenge is how to improve the energy usage and network life in the heterogeneous sensor networks. In this paper, the Mahalanobis distance based K‐Means algorithm is integrated with a novel evolutionary approach for clustering called calf search optimization algorithm (K‐CSOA) that creates energy efficient clusters. The routing in the network is performed using a power efficient ant colony optimization algorithm (ACO). The extensive and multiple simulations are performed to validate the effectiveness of the proposed method. It is observed that the running time of the proposed scheme is lowered due to the implementation of hybrid clustering. The results are validated for various performance metrics wherein the proposed method shows 41.6% and 4.3%–12% reduction in energy expenditure and end‐to‐end delay respectively and around 60% enhancement in the throughput of the network when compared with other similar state‐of‐the‐art methods. In addition to this, statistical analysis is also carried out to identify the performance of the proposed algorithm.

Optimal design of novel precursor materials for the atomic layer deposition using computer-aided molecular design

Nanomaterials and nanostructures with multi-functional properties found widespread applications such as electronics, optics, and coatings that can be fabricated using Atomic Layer Deposition (ALD). ALD is a vapor phase deposition technique to generate thin films of metals and metal oxides on a substrate. In this process, a precursor, which often comprises of organic functional groups that surround the depositing metal, chemisorbs on the substrate or reacts with the surface sites and with each other. Precursor chemisorption on the substrate leads ALD to be a self-limiting process. Thus, the precursor(s) should be chosen in a way to enhance deposition based on the ALD conditions. For a given application, it is practically impossible to carry out a large number of experiments using numerous precursors with varied deposition conditions to find the one that maximizes the growth rate in ALD. In addition, only existing precursors can be tested experimentally. The overall objective of this work is to develop a computational tool for the in-silico design of precursor materials using adsorbate solid solution theory (ASST). In the first part of this paper, we apply the ASST to derive properties of the functional groups present in the precursor using a new Group Contribution Method (GCM). The GCM is successfully derived and compared with the experimental data from ALD. The method shows good agreement and is useful for the design of novel materials. In the second part of this paper, using the thermodynamic properties as obtained from GCM, we develop a computer-aided molecular design (CAMD) framework for the optimal design of novel precursor materials with enhanced deposition properties for the ALD of metal oxides and metals. The metaheuristic efficient ant colony optimization algorithm (EACO) developed in-house is used for both parts. CAMD is a combinatorial optimization methodology, where molecules with optimal desired properties are generated from functional groups. The precursor selection optimization problem is posed as a mixed integer nonlinear programming problem, which is solved using EACO. The ALD growth kinetics is used as an objective of optimization and a solution is validated with thermodynamic constraints. Along with the number of groups, the temperature is also included in the optimization framework as decision variables. Forty-one novel titanium precursor molecular structures for ALD are generated with growth rates ranging from 1.23 Å/cycle to as high as 1.65 Å/cycle. Thus, these precursors have shown growth rates higher than the known titanium precursors. ALD growth rate is found to be a function of the combination of the precursor functional groups as well as temperature with a complex correlation among them.

On the Resilience of Ant Algorithms. Experiment with Adapted MMAS on TSP

This paper focuses on the resilience of a nature-inspired class of algorithms. The issues related to resilience fall under a very wide umbrella. The uncertainties that we face in the world require the need of resilient systems in all domains. Software resilience is certainly of critical importance, due to the presence of software applications which are embedded in numerous operational and strategic systems. For Ant Colony Optimization (ACO), one of the most successful heuristic methods inspired by the communication processes in entomology, performance and convergence issues have been intensively studied by the scientific community. Our approach addresses the resilience of MAX–MIN Ant System (MMAS), one of the most efficient ACO algorithms, when studied in relation with Traveling Salesman Problem (TSP). We introduce a set of parameters that allow the management of real-life situations, such as imprecise or missing data and disturbances in the regular computing process. Several metrics are involved, and a statistical analysis is performed. The resilience of the adapted MMAS is analyzed and discussed. A broad outline on future research directions is given in connection with new trends concerning the design of resilient systems.

AN Efficient Ant Colony Optimization Algorithm for Resource Provisioning in Cloud

Paper Several Ant Colony Optimization (ACO) techniques for Cloud resources management are considered by many researchers. ACO techniques in existence still need some improvements for effective resource management and planning with the heterogeneous and voluminous services offered. Hence, an optimized hybrid scheme that combined deterministic characteristics for exploiting ACO search process is proposed. Spanning Tree (ST) algorithm was chosen in the hybridization that obtained a faster convergence speed, minimized makespan time and throughput that ensured resource utilization in least time and cost. Extensive experiments were conducted in cloudsim simulator provided an efficient result compared to other ACO techniques as it significantly improves performance.

An efficient ant colony optimization algorithm for the blocks relocation problem

In this paper we present an ant colony optimization (ACO) algorithm for the Blocks Relocation Problem (BRP). The method is applied to both versions of the problem most commonly considered in literature, i.e., the restricted (rBRP) and the unrestricted (uBRP) BRP with distinct due dates. In case of the uBRP a new heuristic is proposed and incorporated in a standard greedy algorithm. The performance of the basic greedy approach is enhanced by extending it to the ACO metaheuristic. In it, a novel approach for defining the pheromone matrix is proposed. More precisely, it only stores a small amount of information instead of the complete bay state. Further, we show that the proposed ACO method can easily be adapted for solving the BRP in which the objective function is related to the crane operation time. Our computational results show that the proposed approach manages to outperform existing methods for the BRP.

A new memetic algorithm for multiple graph alignment

A new memetic algorithm for multiple graph alignment

Heterogeneous multi-agent optimization framework with application to synthesizing optimal nuclear waste blends

Multi-agent optimization method is a nature-inspired framework that supports the cooperative search of an optimal solution of an optimization problem by a group of algorithmic agents connected through an environment with certain predefined information sharing protocol. In this work, we propose a novel heterogeneous multi-agent optimization (HTMAO) framework. The proposed framework is validated using a set of benchmark problems a real-world synthesizing radioactive waste blending problem. The optimal radioactive waste blending problem is formulated as a mixed integer nonlinear programming. The total frit used for vitrification process is minimized subject to thermodynamic properties and process model constraints. The model simultaneously determines the optimal decisions that include the combination of the waste tanks that form each waste blend and the amount of frit needed for the vitrification of each waste blend. In developing the HTMAO framework, efficient ant colony optimization algorithms; efficient simulated annealing; efficient genetic algorithm; and sequential quadratic programming solver are considered as algorithmic agents. We illustrate this approach through a real-world case study of the optimal radioactive waste blending of Hanford site in Southern Washington where nuclear waste is stored.

Efficient ant colony optimization for computer aided molecular design: Case study solvent selection problem

In this paper, we propose a novel computer-aided molecular design (CAMD) methodology for the design of optimal solvents based on an efficient ant colony optimization (EACO) algorithm. The molecular design problem is formulated as a mixed integer nonlinear programming (MINLP) model in which a solvent performance measure is maximized (solute distribution coefficient) subject to structural feasibility, property, and process constraints. In developing the EACO algorithm, the better uniformity property of Hammersley sequence sampling (HSS) is exploited. The capabilities of the proposed methodology are illustrated using a real world case study for the design of an optimal solvent for extraction of acetic acid from waste process stream using liquid–liquid extraction. The UNIFAC model based on the infinite dilution activity coefficient is used to estimate the mixture properties. New solvents with better targeted properties are proposed.

Efficient Ant Colony Optimization (EACO) Algorithm for Deterministic Optimization

In this paper, an efficient ant colony optimization (EACO) algorithm is proposed based on efficient sampling method for solving combinatorial, continuous and mixed-variable optimization problems. In EACO algorithm, Hammersley Sequence Sampling (HSS) is introduced to initialize the solution archive and to generate multidimensional random numbers. The capabilities of the proposed algorithm are illustrated through 9 benchmark problems. The results of the benchmark problems from EACO algorithm and the conventional ACO algorithm are compared. More than 99% of the results from the EACO show efficiency improvement and the computational efficiency improvement range from 3% to 71%. Thus, this new algorithm can be a useful tool for large-scale and wide range of optimization problems. Moreover, the performance of the EACO is also tested using the five variants of ant algorithms for combinatorial problems.

Modified Ant Colony Optimization Algorithm with Uniform Mutation using Self-Adaptive Approach

Colony Optimization (ACO) algorithm is a novel meta- heuristic algorithm that has been widely used for different combinational optimization problem and inspired by the foraging behavior of real ant colonies. Ant Colony Optimization has strong robustness and easy to combine with other methods in optimization. In this paper, an efficient ant colony optimization algorithm with uniform mutation operator using self-adaptive approach has been proposed. Here mutation operator is used for enhancing the algorithm escape from local optima. The algorithm converges to the optimal final solution, by gathering the most effective sub-solutions. Experimental results show that the proposed algorithm is better than the algorithm previously proposed.

Ant Colony Optimization for Social Utility Maximization in a Multiuser Communication System

In a multiuser communication system such as cognitive radio or digital subscriber lines, the transmission rate of each user is affected by the channel background noise and the crosstalk interference from other users. This paper presents an efficient ant colony optimization algorithm to allocate each user’s limited power on different channels for maximizing social utility (i.e., the sum of all individual utilities). The proposed algorithm adopts an initial solution that allocates more power on the channel with a lower background noise level. Besides, the cooling concept of simulated annealing is integrated into the proposed method to improve the convergence rate during the local search of the ant colony optimization algorithm. A number of experiments are conducted to validate the effectiveness of the proposed algorithm.

Multiprocessor Scheduling Problem Based on Ant Colony Optimization Algorithm

Multiprocessor Scheduling is a problem of discrete optimization. The strong optimization capacity of Ant Colony Algorithm (ACA) on solving the discrete optimization suggests the feasibility to solve the multiprocessor scheduling problem using the ACA algorithm. Contrary to the shortcomings of traditional ant colony algorithm, this is easy to fall into the local convergence, In order to improve the calculation accuracy. combined of multiprocessor scheduling problem, propose a more efficient ant colony optimization algorithm and present a new state transition rule, at the same time use dynamically update the strategy of ant pheromones and the optimal parameter selection. it can find a better scheduling strategy in a short time, and it has excellent global optimization properties, The simulation results show that the credibility and the validity of the improved ant colony optimization algorithm for multiprocessor scheduling problem.

Simultaneous Determination of Guaifenesin and Theophylline by Chemometrics Methods

In this study, simultaneous spectrophotometry determination of guaifenesin and theophylline in pharmaceuticals by chemometric approaches has been reported. Spectra of mixtures of these drugs were recorded and corresponding first derivatives were calculated. Partial least squares regression (PLS) alone and ant colony optimization (ACO) coupled with PLS were used in analysis of the data. Ant colony system (ACS) as an efficient ACO algorithm was used. In addition, ACS was combined to genetic algorithm (GA) to produce better results. The analytical performances of these chemometric methods were characterized by relative prediction errors. These methods were successfully applied to pharmaceutical formulation.

4th German Conference on Chemoinformatics: 22. CIC-Workshop. November 9–11, 2008, Goslar, Germany

From the 9th to the 11th November 2008, the Chemistry-Information-Computers (CIC) division of the German Chemical Society (GDCh) has invited the chemoinformatics and modeling community to Goslar, Germany to participate in the 4th German Conference on (GCC). Almost 200 participants from 14 countries accepted the invitation making the event again one of the largest chemoinformatics conferences in Europe. The international symposium addressed a broad range of modern research topics in the field of computers and chemistry. The focus was on recent developments and trends in the fields of and Drug Development, Chemical Information, Molecular Modeling, Computational Material Science and Nanotechnology. In addition, other contributions from the field of Computational Chemistry were welcome. The scientific program included one evening lecture [K. Varmuza(Austria)], six plenary lectures [J. Bajorath (Germany), M.R. Berthold (Germany), R. Herges (Germany), S.M. Bachrach (USA), R. C. Glen (UK), M. Elstner (Germany)], 16 general lectures as well as 77 poster presentations. Traditionally, the conference started with a Free-Software-Session and the Chemoinformatics Market Place on Sunday afternoon right before the official conference opening at 5 pm. Numerous Open Source projects from the field of have been presented within the Free-Software-Session in the large lecture hall at the conference hotel 'Der Achtermann in Goslar. In addition two software tutorials have been provided in parallel within the scope of the Chemoinformatics Market Place: One software tutorial was provided by Xemistry GmbH covering some key technologies of their CACTVS application. In the second tutorial Chemical Computing Group offered insights into the new release of MOE. After the official opening at 5 pm and a short introduction of the 10 exhibitors the first day ended with a conference dinner and the evening lecture of Kurt Varmuza on Classification and Characterization of Materials – from Archaeometry to Comets. Besides the scientific program a special highlight of the conference was the FIZ-CHEMIE-Berlin 2008 awards on Monday afternoon. The CIC division awards this price each year to the best diploma thesis and the best PhD in the field of Computational Chemistry. The PhD price was awarded to Dr. Oliver Korb from the group of Prof. Dr. Thomas Exner, Konstanz for his dissertation Efficient Ant Colony Optimization Algorithms for Structure- and Ligand-based Drug Design. The award for the best diploma thesis has gone to Andreas Jahn from the group of Prof. Dr. Andreas Zell, Tubingen with the title Die Erweiterung des Optimal Assignment Kernel (OAK) um konformationelle Flexibilitat. Finally, after 4 years the old GDCh-CIC division board handed over the divisions concerns to the new elected board members during the conference. The success of the German Conference on is definitely a credit to the old CIC board and we want to thank the retired board members Christoph Steinbeck, Gregor Fels and Rainer Herges for their work and engagement. Pictures of the conference are available on the GDCh webserver: http://www.gdch.de/strukturen/fg/cic/tagungen/gcc_2008.htm

Efficient ant colony optimization algorithms for structure- and ligand-based drug design

Efficient ant colony optimization algorithms for structure- and ligand-based drug design