Booncharoen Sirinaovakul

The best-so-far selection in Artificial Bee Colony algorithm

The Artificial Bee Colony (ABC) algorithm is inspired by the behavior of honey bees. The algorithm is one of the Swarm Intelligence algorithms explored in recent literature. ABC is an optimization technique, which is used in finding the best solution from all feasible solutions. However, ABC can sometimes be slow to converge. In order to improve the algorithm performance, we present a modified method for solution update of the onlooker bees in this paper. In our method, the best feasible solutions found so far are shared globally among the entire population. Thus, the new candidate solutions are more likely to be close to the current best solution. In other words, we bias the solution direction toward the best-so-far position. Moreover, in each iteration, we adjust the radius of the search for new candidates using a larger radius earlier in the search process and then reduce the radius as the process comes closer to converging. Finally, we use a more robust calculation to determine and compare the quality of alternative solutions. We empirically assess the performance of our proposed method on two sets of problems: numerical benchmark functions and image registration applications. The results demonstrate that the proposed method is able to produce higher quality solutions with faster convergence than either the original ABC or the current state-of-the-art ABC-based algorithm.

ABC-GSX: A hybrid method for solving the Traveling Salesman Problem

An optimization problem is a problem of finding the best solution from all possible solutions. In most computer science and mathematical applications, the decision to select the best solution is not polynomially bounded. Heuristics approaches are thus often considered to solve such NP-hard problems. In our work, we focus on developing a heuristic method to solve a combinatorial optimization problem known as the Traveling Salesman Problem or TSP. Our technique implements the Artificial Bee Colony algorithm, which is inspired by the decision making process of the honey bees in finding optimal food sources. We extend the ABC algorithm with Greedy Subtour Crossover to improve the precision. In this hybrid procedure, the exploitation process in the ABC algorithm is improved upon by the Greedy Subtour Crossover method. The new proposed method is called ABC-GSX. We then empirically assess performance of our proposed work using functions from a standard TSP library. Experimental results show improvements in both precision and computational time compared to techniques presented in recent literatures.

The best-so-far ABC with multiple patrilines for clustering problems

Clustering is an important process in many application domains such as machine learning, data mining, pattern recognition, image analysis, information retrieval, and bioinformatics. The main objective of clustering is to search for hidden patterns that may exist in datasets. Since the clustering problem is considered to be NP-hard, previous research has applied bio-inspired heuristic methods to solve such problems. In this paper we propose an effective method for clustering using an algorithm inspired by the decision making processes of bee swarms. The algorithm is called the Best-so-far Artificial Bee Colony with multiple patrilines. In the Best-so-far method, the solution direction is biased toward the Best-so-far solution rather than a neighboring solution proposed in the original Artificial Bee Colony algorithm. We introduce another bee-inspired concept called multiple patrilines to further improve the diversity of solutions and allow the calculations to be distributed among multiple computing units. We empirically assess the performance of our proposed method on several standard datasets taken from the UCI Machine Learning Repository. The results show that the proposed method produces solutions that are as good as or better than the current state-of-the-art clustering techniques reported in the literature. Furthermore, to demonstrate the computing performance and scalability of the algorithm, we assess the algorithm on a large disk drive manufacturing dataset. The results indicate that our distributed Best-so-far approach is scalable and produces good solutions while significantly improving the processing time.

Artificial bee colony algorithm on distributed environments

Artificial Bee Colony (ABC) is a metaheuristic approach in which a colony of artificial bees cooperates in finding good solutions for numerical optimization problems. ABC is adopted widely for use in several domains of solution optimization. However, the algorithm generally requires a considerably large computational time and resources. In order to enhance the performance of this algorithm for a large problem size, we introduce a distributed version of ABC. In our parallel algorithm, the entire bee colony is decomposed into several subgroups. Each subgroup then performs a local search concurrently on each processor node. The local best solutions are then exchanged among processor nodes. The algorithm implementation utilizes the message passing technique as a communication paradigm. We then empirically assess the performance based on both result accuracy and algorithms efficiency. The experimental results show improvement in both solution quality and computing time when comparing to the sequential ABC algorithm.

Cost optimal scheduling in IaaS for dependent workload with particle swarm optimization

Optimizing cloud provisioning for scientific workflow applications is a challenging problem, since the workflows generally contain dependency between tasks and require specific deadlines. Usually, cloud providers offer many options to the consumers. These options include the number of virtual machines, the type of each virtual machine and the purchasing method for each machine. Currently, cloud provisioning cost optimization is an active research topic. Most of this literature is concerned with task scheduling, cloud option selection, and cloud option selection for scientific workflow applications. However, research that attempts to find solutions which cover both cloud option selection and workflow task scheduling is very limited. In this paper, we focus on optimizing the cost of purchasing infrastructure-as-a-service cloud capabilities to achieve scientific work flow execution within the specific deadlines. The proposed system considers the number of purchased instances, instance types, purchasing options, and task scheduling as constraints in an optimization process. Particle swarm optimization augmented with a variable neighborhood search technique is used to find the optimal solution. Our approach finds the configurations of purchasing options with the optimum budget for a specified workflow application based on the required performance. The solutions from the proposed system show promising performance from the perspectives of the total cost and fitness convergence when compared with other state-of-the-art algorithms.

In Depth Analysis of The CMOS OTA-Based Floating Inductors

Commonly known, the gyrator-based OTA simulated floating inductor can be divided into two categories; 3-OTA and 4-OTA structure which perform identically in the ideal phenomena where all OTAs nonidealities i.e. parasitic elements, effect of finite open-loop bandwidth and noise have been neglected. It has been found in R. Banchuin et al. (2005) that the 4-OTA-based floating inductor has better functional and noise performances than its 3-OTA counterpart in the practical phenomena where all of the cited nonidealities included. However, this conclusion has been made based upon the assumption that all OTAs are of the bipolar type. Therefore, due to the rise of the age of CMOS technology; an attempt to find the difference between the 3-CMOS-OTA and 4-CMOS-OTA based floating inductors has been made. Including all of the cited nonidealities, the 4-CMOS-OTA-based floating inductor also has both better functional and noise performances than its 3-CMOS-OTA counterpart. This conclusion strengthens the superiority of the 4-OTA structure over the 3-OTA counterpart since it has been found to be independent of the basis technology and also supports the design guideline proposed in R. Banchuin et al. (2005)

Virtual Laboratory: A Distributed Collaborative Environment

This article proposes the design framework of a distributed, real‐time collaborative architecture. The architecture concept allows information to be fused, disseminated, and interpreted collaboratively among researchers who live across continents in real‐time. The architecture is designed based on the distributed object technology, DCOM. In our framework, every module can be viewed as an object. Each of these objects communicates and passes data with one another via a set of interfaces and connection points. We constructed the virtual laboratory based on the proposed architecture. The laboratory allows multiple analysts to collaboratively work through a standard web‐browser using a set of tools, namely, chat, whiteboard, audio/video exchange, file transfer and application sharing. Several existing technologies are integrated to provide collaborative functions, such as NetMeeting. Finally, the virtual laboratory quality evaluation is described with an example application of remote collaboration in satellite image fusion and analysis.

A stochastic knowledge-based Thai text-to-speech system

We describe the development of our Thai text-to-speech (TTS) system. Thai TTS system transforms Thai texts to the sequence of appropriate sounds for Thai speech. Thai complexity requires approximately four hundred rules including main and specific rules to derive most pronunciations for our rule-based approach grounded on Thai syllable structure analysis. An exception dictionary that covers anomalous pronunciations and a rule inference engine that determines sentence structures are included to improve the quality of Thai TTS. Speech generation by concatenative synthesis is a sequential step that transforms sound symbols to synthetic speech. We have tested our system with magazine and internet articles, together with articles from the experiments of other researchers and we report the results of this informal evaluation. Most syllables from Thai written strings can be converted to phonetic symbols. With a compact Thai unit inventory, the concatenative synthesis system can synthesize synthetic speech covering many Thai syllables.

Thai syllable-based Information Extraction using Hidden Markov Models

Information Extraction (IE) is a method which analyzes the information and retrieves significant segments or fields for insertion into tables or databases by automatic extraction. In this paper, we employ a statistical model for an IE system. Thai syllable-based information extraction using Hidden Markov Models (HMM) is our proposed method for automated information extraction. In our system, we develop a non-dictionary based method which requires a rule-based system for syllable segmentation. We employ a Viterbi algorithm, which is a statistical system for learning/testing our corpus, and extract the required fields from the information in corpus.

Best-So-Far ABC Based Nanorobot Swarm

Nanotechnology has continuously advanced with many tremendous promises to offer. The coming of nanorobots seems inevitable. Medical applications would be one of the first utilization of nanorobots. Nanorobot may become a solution for many currently incurable diseases as it can be viewed as a medical instrument that can be released inside human body for drug delivery and diagnosis [1-3]. In order to effectively utilize nanorobots, the concept of swarm intelligence is extensively studied in the literature [4-8]. In this study, we proposed a framework based on swarm intelligence concept for nanorobot control in medical applications. We adopted a variation of Artificial Bee Colony (ABC) called Best-so-far ABC to regulate nanorobot behavior. Best-so-far ABC biases solutions toward the best position of current iteration. The proposed framework is demonstrated in wounds healing application, nanorobots operate inside a network of blood vessels as artificial platelets to assist in hemostasis process to stop bleeding. Nanorobots must manage to attend to all multiple wounds. The demonstration is one example of possible medical applications of the swarm-intelligence-based nanorobot framework.