Wavelet Based Image Compression Using Soft Computing Techniques

INTRODUCTION: The artificial bee colony (ABC) algorithm is a nature-inspired technique used for solving different optimization problems. This paper presents a multi-hive ABC algorithm for solving constrained benchmark functions of CEC2006. The CEC2006 data set contains the global benchmark functions with different design variables, number and type of constraints. OBJECTIVES: The objective of the proposed work is to design and apply the GPGPU based multi-hive ABC algorithm to solve constrained optimization problems. METHODS: The proposed approach is a multi-population coarse-grained system implemented using General Purpose Graphics Processing Unit (GPGPU). The performance of the proposed approach is compared with the serial ABC algorithm for eleven benchmark functions and results in the literature. The multi-hive ABC algorithm has multiple hives, each running separate ABC algorithm on different cores of GPGPU. RESULTS: The proposed approach provides global best solutions in significantly reduced time for all benchmark functions. The speed-up obtained is approximately 7X to 9X. The GPGPU device utilization is approximately 57% to 91%. CONCLUSION: The GPGPU based multi-hive ABC algorithm is a found good with respect to best results, speed up factor and GPU utilization to solve constrained optimization problems.

This paper discusses an approach to automating CAD model acquisition by creating binary space partitioning (BSP) trees from laser range data. Distinct views of the object, each represented by a BSP tree, are then merged using set operations. BSP trees have proven their utility in 3D modeling, graphics and image processing, and their tree structure allows efficient algorithms to be developed that are compact and numerically robust. These properties are of primary importance when considering an intermediate representation between raw sensor data and existing CAD models.

Swarm intelligence is briefly defined as the collective behavior of decentralized and self-organized swarms. Self-organization and labor division are the two key components of swarm intelligence. Artificial Bee Colony (ABC) algorithm is one of the most recent swarm intelligence-based algorithms. The behavior of bees in ABC algorithm satisfies the self-organization features, but there is no specific labor division mechanism in ABC algorithm. In this work, we propose an improved ABC algorithm called labor division artificial bee colony (LDABC) algorithm by incorporating the labor division mechanism into ABC algorithm, which is achieved by individual specialization and role plasticity. We specify three different search methods for employed bees, onlooker bees and scout bees to realize individual specialization, these search methods are related to food source quality, enable bees to maximize exploitation of food source. Role plasticity is achieved by combining with cellular automata, where the roles of bees are not static but vary with their surrounding environment, enable bees not to limit to one search method. The different search modes and the flexibility of the search behaviors make our algorithm achieve a better balance between exploration and exploitation. The experimental results tested on 13 benchmark functions and CEC-2013 test functions demonstrate a competitive performance.

The artificial bee colony (ABC) algorithm is an important branch of evolutionary algorithms and has been shown to be competitive with other algorithms for solving optimisation problems. However, there are always some insufficiencies in ABC algorithm such as lower convergence speed and easily get trapped in the local optima. To increase depth search capabilities of onlooker bees and ensure scout bees do not discard the current optimal solution, we proposed a modified ABC algorithm (denoted as ORABC) based on the optimisation strategy and retained strategy of the best individual in this paper. Compared with ABC algorithm, ABC* (added retained strategy to ABC) and ORABC algorithm, experiments are conducted on a set of numerical benchmark functions. The numerical simulation results demonstrate that ORABC algorithm improves the convergence characteristics of ABC algorithm and provides very remarkable performance in solving complex numerical optimisation problems compared to original algorithm.

Gbest-guided artificial bee colony algorithm for numerical function optimization

Abstract-- The main objective of this paper is to examine the performance of Neuro- Statistical sparse transformation function for implementation in a still image vector coding based compression system. This paper discusses the important features of low bit-rate image coding which is based on recent developments in the theory of multivariate nonlinear piecewise polynomial approximation in still images. It combines Binary Space Partition (BSP) scheme with Geometric Wavelet (GW) tree approximation so as to efficiently capture curve singularities and provide a sparse representation of the image. The quality of the reconstructed image is measured objectively using Peak Signal to Noise Ratio. Experimental results show that the proposed image compression system yields higher compression with minimal loss.

Dust aerosol size distribution is an important source of information about atmospheric aerosols, and it can be determined from multiwavelength extinction measurements. This paper describes a stochastic inverse technique based on artificial bee colony (ABC) algorithm to invert the dust aerosol size distribution by light extinction method. The direct problems for the size distribution of water drop and dust particle, which are the main elements of atmospheric aerosols, are solved by the Mie theory and the Lambert-Beer Law in multispectral region. And then, the parameters of three widely used functions, i.e. the log normal distribution (L-N), the Junge distribution (J-J), and the normal distribution (N-N), which can provide the most useful representation of aerosol size distributions, are inversed by the ABC algorithm in the dependent model. Numerical results show that the ABC algorithm can be successfully applied to recover the aerosol size distribution with high feasibility and reliability even in the presence of random noise.

This study addresses the optimization of humanitarian logistics during natural disasters by implementing the Artificial Bee Colony (ABC) algorithm. Natural disasters frequently disrupt infrastructure and supply chains, complicating aid distribution. The paper reviews bio-inspired and metaheuristic algorithms, with a focus on ABC, which mimics the foraging behavior of bees to explore and exploit optimal solutions efficiently. The ABC algorithm is compared against a genetic algorithm using statistical tests, including Shapiro-Wilk, F-test, and Z-test, confirming ABC's superior performance in resource distribution. The research identifies key challenges such as uncertain data, limited transportation capacity, and the need for equitable aid allocation. Numerical experimentation demonstrates that the ABC algorithm delivers higher efficiency and effectiveness in planning relief operations. Additionally, the study presents a software interface to facilitate parameter configuration and result visualization. This work contributes to the field by introducing the ABC algorithm in a new context, offering a reliable tool for disaster response planning.

The FIR filter has been widely used in the digital signal processing and other engineering calculations for its strictly linear phase and stability performance. The artificial bee colony (ABC) algorithm is a new kind of swarm intelligence optimization algorithm which has a strong search mechanism. This paper applies ABC into the linear-phase FIR filter design based on frequency sampling method which can search the optimal frequency sampling points in order to obtain the top minimum stop-band attenuation. By comparing the maximum stop-band attenuation of the traditional frequency sampling method, common genetic algorithm (GA) and ABC algorithm, the experiment shows that the application of ABC algorithm used in FIR filter design is effective.

In this study, an ABC-Local Search (ABC-Ls) method was proposed by including a new local search procedure into the standard artificial bee colony (ABC) algorithm to perform the parameter estimation of photovoltaic systems (PV). The aim of the proposed ABC-Ls method was to improve the exploration capability of the standard ABC with a new local search procedure in addition to the exploitation and exploration balance of the standard ABC algorithm. The proposed ABC-Ls method was first tested on 15 well-known benchmark functions in the literature. In the results of the Friedman Mean Rank test used for statistical analysis, ABC-Ls method successfully ranked first with a value of 1.300 in benchmark functions. After obtaining successful results on the benchmark tests, the proposed ABC-Ls method was applied to the single diode, double diode and Photowatt-PWP-201 PV modules of PV systems for parameter estimations. In addition, the proposed ABC-Ls method has been applied to the KC200GT PV module for parameter estimation under different temperature and irradiance conditions of the PV modules. The success of ABC-Ls method was compared with genetic algorithm (GA), particle swarm optimization (PSO) algorithm, ABC algorithm, tree seed algorithm (TSA), Jaya, Atom search optimization (ASO). The comparison results were presented in tables and graphics in detail. The RMSE values for the parameter estimation of single diode, double diode and Photowatt-PWP-201 PV module of the proposed ABC-LS method were found as 9.8602E−04, 9.8257E−04 and 2.4251E−03, respectively. In this context, the proposed ABC-LS method has been compared with the literature for parameter estimation of single diode, double diode and Photowatt-PWP-201 PV module and it has been found that it provides a parameter estimation similar or better than other studies. The proposed ABC-Ls method for parameter estimation of the KC200GT PV module under different conditions is shown in convergence graphs and box plots, where it achieves more successful, effective and stable results than GA, PSO, ABC, TSA, Jaya and ASO algorithms.

An effective non-revisiting artificial bee colony (NrABC) algorithm based on the paradigm of artificial bee colony (ABC) is developed in this paper. NrABC is applied to tackle the synthesis of phased linear arrays. Pros and cons of NrABC is provided along with a comparison to standard ABC. Binary space partitioning tree structure is used to record history evolutionary information. Non-revisiting scheme assures NrABC keeping good diversity of population. Moreover, scout bee stage is discarded in NrABC which also removes an algorithmic parameter of standard ABC. Three phased array synthesis examples are employed to study the performance of NrABC. It turns out that under the same experimental configuration, NrABC outperforms standard ABC in terms of both solution quality and reliability in repeated runs.

One of polymeric biological molecules called ribonucleic acid (RNA) is involved in diverse biological roles in regulation of gene expression. Generally, secondary structures of RNA determine their functions in living organisms. In medical field, various structures of synthetic RNAs are much needed at present. Recently, an RNA design tool has been developed to identify optimal RNA sequences for the desired interacting RNAs based on Genetic Algorithm (GA). In an attempt to improve the design tool, we applied Artificial Bee Colony (ABC) algorithm, which can outperform several population-based methods including GA. To achieve this goal, we developed an ABC-based RNA design tool and employed this tool to design 6 different interacting RNA models. The design performance from ABC-based tool for all models were compared with that from the existing GA-based tool. It was found that the ABC-based tool and the GA-based tools equally perform in all 6 models. Both tools can achieve the fitness score above 0.996. Although the ABC-based tool could be used to design interacting RNA more reliably, it seems to require more design time, especially in the models with larger RNA length. Thus, with further improvement, this tool shall be a promising tool for designing synthetic RNA devices for biomedical applications.

Artificial Bee Colony (ABC) algorithm has various features that make it more attractive than other algorithms. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a new MPPT system has been suggested for photovoltaic (PV)-DC motor–pump system by designing two PI controllers via ABC algorithm. The first one is used to reach MPPT by monitoring the voltage and current of the PV array and adjusting the duty cycle of the DC/DC converter. The second PI controller is designed for speed control of DC series motor by setting the voltage fed to the DC series motor through another DC/DC converter. The suggested design problem of MPPT and speed controller is formulated as an optimization task which is solved by ABC to search for optimal parameters of PI controllers. Simulation results have shown the validity of the developed technique in delivering MPPT to DC series motor–pump system under atmospheric conditions and tracking the reference speed of motor. Moreover, the performance of the ABC algorithm is compared with Genetic Algorithm (GA) for various disturbances to prove its robustness.

The Software Cost Estimation (SCE) is one of most important issues in the cycle of development, management decision, and in the quality of software project. In the case of the lack of certainty of the exact cost for software projects' development, companies encounter with numerous challenges. Also, due to inaccurate cost estimates, making wrong decisions by project managers make irreparable damage. To reach this propos, the effective factors for developing software projects should be evaluated to ensure of the projects success. COCOMO model is the main model for SCE which acts based on criteria and quantities such as number of Line of Code (LOC) or the Function Point Analysis (FPA). Research in recent years has shown that COCOMO model has not a good performance in the SCE. In this paper, we studied SCE by using a hybrid of Genetic Algorithm (GA) and Artificial Bee Colony (ABC) which are Meta-Heuristic Algorithms. Test results show that proposed model, GA and ABC algorithms have less MRE errors values than the COCOMO model. Also, the hybrid model has better convergence comparing with the GA and ABC algorithms.

The presently developed paper deals with the design of garlic expert systems using machine learning algorithms to advice the farmers in villages through online. An expert system is a computer program that simulates the judgments and behavior of a human or an organization that has expert knowledge and experience in a particular field. Typically, such a system contains a knowledge base containing accumulated experience and a set of rules for applying the knowledge base to each particular situation that is described to the program. Machine learning is a branch of computer science covering software that uses data to improve it's accuracy at some given task. Conrad uses supervised learning, which is one component of machine learning. Artificial Bee Colony (ABC) Algorithm is one of the mostly used Machine Learning Technique. By using this ABC Algorithm we developed a new 'Garlic Expert Advisory System'. This system is mainly aimed to identify the diseases and disease management in garlic crop production to advise the farmers in the villages on line to obtain standardized yields. This advisory system is designed by using Java Server Pages (JSP) as front end and MYSQL as backend.