An Improved Gbest Guided Artificial Bee Colony (IGGABC) Algorithm for Classification and Prediction Tasks

Shah, Habib; Herawan, Tutut; Ghazali, Rozaida; Naseem, Rashid; Aziz, Maslina Abdul; Abawajy, Jemal

doi:10.1007/978-3-319-12637-1_70

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…Motivated by the foraging behavior of honeybees [14], researchers have initially proposed ABC algorithm for solving various optimization problems. ABC is a relatively new populationbased meta-heuristic approach and is further improved by researchers for many applications [15][16]. To increase the performance of the standard ABC algorithm in exploration and exploitation procedures, researchers improved the standard ABC by using different strategies like hybridization, modification, gbest, global best, guided so best so far and other strategies [4,[17][18].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Guided Artificial Bee Colony Algorithm for Numerical Function Optimization

Shah

Herawan

Naseem

et al. 2014

Lecture Notes in Computer Science

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Abstract. Many different earning algorithms used for getting high performance in mathematics and statistical tasks. Recently, an Artificial Bee Colony (ABC) developed by Karaboga is a nature inspired algorithm, which has been shown excellent performance with some standard algorithms. The hybridization and improvement strategy made ABC more attractive to researchers. The two famous improved algorithms are: Guided Artificial Bee Colony (GABC) and Gbest Guided Artificial Bee Colony (GGABC), are used the foraging behaviour of the gbest and guided honey bees for solving optimization tasks. In this paper, GABC and GGABC methods are hybrid and so-called Hybrid Guided Artificial Bee Colony (HGABC) algorithm for strong discovery and utilization processes. The experiment results tested with sets of numerical benchmark functions show that the proposed HGABC algorithm outperforms ABC, PSO, GABC and GGABC algorithms in most of the experiments.

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Section: Introductionmentioning

confidence: 99%

Hybrid Guided Artificial Bee Colony Algorithm for Numerical Function Optimization

Shah

Herawan

Naseem

et al. 2014

Lecture Notes in Computer Science

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“…This difficulty can be solved by the presence of nutritional expert. By the rapid growth of technology, the result of research and training of well-developed people, there is a type of technology which adopts the process of human's way of thinking which is called as Artificial Intelligence (AI) [5,6,7]. Artificial Intelligence (AI) has many types of application, and one of them is expert system.…”

Section: Introductionmentioning

confidence: 99%

Identifying Severe Malnutrition in Children with an Expert System

2021

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Expert system is a computer program which is designed for modelling the ability of problem solving as it is an expert (human expert). The expert system method used is the forward chaining method which is the inference method that is doing logical reasoning from the problem to its solution. The aim of this research is to design and develop an expert system that is able to identify the severe malnutrition on children from the age of 0 - 5 years old. The knowledge is derived from the question askedto a nutrition expert. The data are taken from the questions asked to the user and when all of the questions has been answered, then the goal will be appeared which shows the nutrition status. This system application will enable the user to diagnose the nutrition/disease that affects children and get the solution. This system can be used by any kind of user due to the easy access. This system is also put the important information about the severe malnutrition and the recent news of children’s health so it will add more knowledge for the parents about the importance of severe malnutrition’s prevention.

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“…By utilizing the Data Mining tools, the data extraction process can be performed optimally [6]. This allows us to predict the behavior and future trend, enabling businesses to make proactive decisions based on certain knowledge [7]. Data Mining is capable of answering the business issues that are traditionally too long to be resolved [8] Data Mining browse the database to find hidden patterns.…”

Section: Introductionmentioning

confidence: 99%

K- Means Clustering on Based Classification Method of Sales Agent

2021

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Agent is one of very important assets for distributors. A better knowledge of the agents and their behavior is required, particularly to support decisions related to the company's business strategy and to manage a better relationship with distributors. Such knowledge can be obtained by classifying agents based on their behavior through historical data, such as the sale and purchase transaction data. One approach that can be done is a segmentation approach can be done by dividing the agents into several segments. In this paper, Data Mining techniques i.e. K-means clustering method is exploredto classify sales agents. By implementing k-means, the knowledge about the best agents can be acquired along with the agents that have least contribution to the distributor.

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An Improved Gbest Guided Artificial Bee Colony (IGGABC) Algorithm for Classification and Prediction Tasks

Cited by 10 publications

References 16 publications

Hybrid Guided Artificial Bee Colony Algorithm for Numerical Function Optimization

Hybrid Guided Artificial Bee Colony Algorithm for Numerical Function Optimization

Identifying Severe Malnutrition in Children with an Expert System

K- Means Clustering on Based Classification Method of Sales Agent

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