Global gbest guided-artificial bee colony algorithm for numerical function optimization

Numerous computational algorithms are used to obtain a high performance in solving mathematics, engineering and statistical complexities. Recently, an attractive bio-inspired method—namely the Artificial Bee Colony (ABC)—has shown outstanding performance with some typical computational algorithms in different complex problems. The modification, hybridization and improvement strategies made ABC more attractive to science and engineering researchers. The two well-known honeybees-based upgraded algorithms, Gbest Guided Artificial Bee Colony (GGABC) and Global Artificial Bee Colony Search (GABCS), use the foraging behavior of the global best and guided best honeybees for solving complex optimization tasks. Here, the hybrid of the above GGABC and GABC methods is called the 3G-ABC algorithm for strong discovery and exploitation processes. The proposed and typical methods were implemented on the basis of maximum fitness values instead of maximum cycle numbers, which has provided an extra strength to the proposed and existing methods. The experimental results were tested with sets of fifteen numerical benchmark functions. The obtained results from the proposed approach are compared with the several existing approaches such as ABC, GABC and GGABC, result and found to be very profitable. Finally, obtained results are verified with some statistical testing

A quick gbest guided artificial bee colony algorithm for stock market prices prediction

The objective of this work is to present a Quick Gbest Guided artificial bee colony (ABC) learning algorithm to train the feedforward neural network (QGGABC-FFNN) model for the prediction of the trends in the stock markets. As it is quite important to know that nowadays, stock market prediction of trends is a significant financial global issue. The scientists, finance administration, companies, and leadership of a given country struggle towards developing a strong financial position. Several technical, industrial, fundamental, scientific, and statistical tools have been proposed and used with varying results. Still, predicting an exact or near-to-exact trend of the Stock Market values behavior is an open problem. In this respect, in the present manuscript, we propose an algorithm based on ABC to minimize the error in the trend and actual values by using the hybrid technique based on neural network and artificial intelligence. The presented approach has been verified and tested to predict the accurate trend of Saudi Stock Market (SSM) values. The proposed QGGABC-ANN based on bio-inspired learning algorithm with its high degree of accuracy could be used as an investment advisor for the investors and traders in the future of SSM. The proposed approach is based mainly on SSM historical data covering a large span of time. From the simulation findings, the proposed QGGABC-FFNN outperformed compared with other typical computational algorithms for prediction of SSM values

ABC Algorithm for Combinatorial Testing Problem

Computer software is in high demand everywhere in the world. The high dependence on software makes software requirements more complicated. As a result, software testing tasks get costlier and challenging due to a large number of test cases, coupled with the vast number of the system requirements. This challenge presents the need for reduction of the system redundant test cases. A combinatorial testing approach gives an intended result from the optimization of the system test cases. Hence, this study implements a combinatorial testing strategy called Artificial Bee Colony Test Generation (ABC-TG) that helps to get rid of some of the current combinatorial testing strategies. Results obtained from the ABC-TG were benchmarked with the results obtained from existing strategies in order to determine the efficiency of the ABC-TG. Finally, ABC-TG shows the efficiency and effectiveness in terms of generating optimum test cases size of some of the case studies and a comparable result with the existing combinatorial testing strategies

Cooperative guided local search

Over the past few decades, meta-heuristic algorithms (MHs) have proven to be powerful tools for dealing with difficult combinational optimization problems (COPs). These techniques can obtain high quality solutions within reasonable computational time for many hard ,.' A' problems. Among these methods, guided local search (GLS) is p'femising one. The proximate optimality principle (POP), an underlying assumption in most meta-heuristics, assumes that good solutions have similar structures. Structures which are common to good solutions are more likely to be part of the best solution. In this thesis we discuss how the performance of the GLS can be further enhanced through designing a cooperative mechanism based on the proximate optimality principle (POP). The approach that we took was to search for solutions using multiple agents, each of which running a copy of GLS. These agents benefit from each other through the exchange of information based on POP. We suggest based on POP that common features that appear in many locally optimal solutions of GLS agents are more likely to be parts of the globally optimal solution. Thus, this property should be taken into consideration during the search. We call this framework Population-based GLS (P-GLS). P-GLS shows its efficiency and effectiveness to converge quickly to promising regions of the search space in an intelligent manner. Four P-GLS versions are proposed which enhance the performance of P-GLS. These algorithms are extensively studied and tested on Traveling salesman problem (TSP), Multidimensional Knapsack Problem (MKP) and Field Workforce Scheduling Problem (FWSP). Computational results confirm the effectiveness of P-GLS compared to original GLS and other well known MHs.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Population-Based Guided Local Search: Some preliminary experimental results

Based on the Proximate Optimality Principle in metaheuristics, a Population Based Guided Local Search (P-GLS) framework for dealing with difficult combinatorial optimization problems is suggested in this paper. In P-GLS, several guided local search (GLS) procedures (agents) run in a parallel way. These agents exchange information during some time points in the search. The information exchanged is the best solutions found so far by these agents. Each agent use such information to adjust its search behavior for moving to a more promising search region. Some preliminary experiments have been conducted on the traveling salesman problem to study the effectiveness of P-GLS. © 2010 IEEE

Pairwise Test Data Generation based on Flower Pollination Algorithm

Owing to an exponential increase in computational time associated with increasing number of system components, exhaustive testing is increasingly becomes impractical. Here, many researchers opt to adopt pairwise testing to minimize the overall number of tests. Recently, many existing works are focusing on the use of Search-Based algorithms as the basis of the implementation algorithm; however, there is no single strategy that can be the best for all cases. Currently, researches on Flower Pollination Algorithm (FPA) are very active and its applications have been proven successes to solve many problems. This paper proposes a new search-based strategy for generating the pairwise test suite, called Pairwise Flower Strategy (PairFS). The main feature of PairFS is that it is the first pairwise strategy that adopts FPA as its core implementation. To evaluate and benchmark our proposed strategy against existing strategies, several existing comparative experiments are adopted. The results of the experiment show that in many cases PairFS are more efficient than the existing strategies in terms of the test suite size

PARALLEL GUIDED LOCAL SEARCH AND SOME PRELIMINARY EXPERIMENTAL RESULTS FOR CONTINUOUS OPTIMIZATION

This paper proposes a Parallel Guided Local Search (PGLS) framework for continuous optimization. In PGLS, several guided local search (GLS) procedures (agents) are run for solving the optimization problem. The agents exchange information for speeding up the search. For example, the information exchanged could be knowledge about the landscape obtained by the agents. The proposed algorithm is applied to continuous optimization problems. The preliminary experimental results show that the algorithm is very promising

A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction

The objective of this work is to present a Quick Gbest Guided artificial bee colony (ABC) learning algorithm to train the feedforward neural network (QGGABC-FFNN) model for the prediction of the trends in the stock markets. As it is quite important to know that nowadays, stock market prediction of trends is a significant financial global issue. The scientists, finance administration, companies, and leadership of a given country struggle towards developing a strong financial position. Several technical, industrial, fundamental, scientific, and statistical tools have been proposed and used with varying results. Still, predicting an exact or near-to-exact trend of the Stock Market values behavior is an open problem. In this respect, in the present manuscript, we propose an algorithm based on ABC to minimize the error in the trend and actual values by using the hybrid technique based on neural network and artificial intelligence. The presented approach has been verified and tested to predict the accurate trend of Saudi Stock Market (SSM) values. The proposed QGGABC-ANN based on bio-inspired learning algorithm with its high degree of accuracy could be used as an investment advisor for the investors and traders in the future of SSM. The proposed approach is based mainly on SSM historical data covering a large span of time. From the simulation findings, the proposed QGGABC-FFNN outperformed compared with other typical computational algorithms for prediction of SSM values

SURVEY ON INPUT OUTPUT RELATION BASED COMBINATION TEST DATA GENERATION STRATEGIES

ABSTRACT Combinatorial test data generation strategies have been known to be effective to detect the fault in the product due to the interaction between the product's features. Over the years, many combinatorial test data generation strategies have been developed supporting uniform and variable strength interactions. Although useful, these existing strategies are lacking the support for Input Output Relations (IOR). In fact, there are only a handful of existing strategies addresses IOR. This paper will review the existing combinatorial test data generation strategies supporting the IOR features specifically taking the nature inspired algorithm as the main basis. Benchmarking results illustrate the comparative performance of existing nature inspired algorithm based strategies supporting IOR

Survey on input output relation based combination test data generation strategies

Combinatorial test data generation strategies have been known to be effective to detect the fault in the product due to the interaction between the product’s features. Over the years, many combinatorial test data generation strategies have been developed supporting uniform and variable strength interactions. Although useful, these existing strategies are lacking the support for Input Output Relations (IOR). In fact, there are only a handful of existing strategies addresses IOR. This paper will review the existing combinatorial test data generation strategies supporting the IOR features specifically taking the nature inspired algorithm as the main basis. Benchmarking results illustrate the comparative performance of existing nature inspired algorithm based strategies supporting IOR