bee colony optimization
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2022 ◽  
pp. 1043-1058
Author(s):  
Rashmi Rekha Sahoo ◽  
Mitrabinda Ray

The primary objective of software testing is to locate bugs as many as possible in software by using an optimum set of test cases. Optimum set of test cases are obtained by selection procedure which can be viewed as an optimization problem. So metaheuristic optimizing (searching) techniques have been immensely used to automate software testing task. The application of metaheuristic searching techniques in software testing is termed as Search Based Testing. Non-redundant, reliable and optimized test cases can be generated by the search based testing with less effort and time. This article presents a systematic review on several meta heuristic techniques like Genetic Algorithms, Particle Swarm optimization, Ant Colony Optimization, Bee Colony optimization, Cuckoo Searches, Tabu Searches and some modified version of these algorithms used for test case generation. The authors also provide one framework, showing the advantages, limitations and future scope or gap of these research works which will help in further research on these works.


Author(s):  
Aleksandar Jovanović ◽  
Dušan Teodorović

The superstreet intersection (or restricted crossing U-turn-, J-turn intersection) fixed-time traffic control system was developed in this study. The optimal (or near-optimal) values of cycle length, splits, and offsets were discovered by minimizing the experienced travel time of all network users traveling through the superstreet intersection. The optimization procedure used was based on the bee colony optimization (BCO) metaheuristic. The BCO is a stochastic, random-search, population-based technique, inspired by the foraging behavior of honey bees. The BCO belongs to the class of swarm intelligence methods. A set of numerical experiments was performed. Superstreet intersection configurations that allowed direct left turns from the major street, as well as configurations with no direct left turns, were analyzed within numerical experiments. The obtained results showed that BCO outperformed the traditional Webster approach in the superstreet geometrical configurations considered.


2021 ◽  
Author(s):  
Marjana Čubranić-Dobrodolac ◽  
Libor Švadlenka ◽  
Svetlana Čičević ◽  
Aleksandar Trifunović ◽  
Momčilo Dobrodolac

2021 ◽  
Author(s):  
Mohammed Alweshah ◽  
Muder Almiani ◽  
Nedaa Almansour ◽  
Saleh Al khalaileh ◽  
Hamza Aldabbas ◽  
...  

Abstract The vehicle routing problem (VRP) is one of the challenging problems in optimization and can be described as combinatorial optimization and NP-hard problem. Researchers have used many artificial intelligence techniques in order to try to solve this problem. Among these techniques, metaheuristic algorithms that can perform random search are the most promising because they can be used to find the right solution in the shortest possible time. Therefore, in this paper, the Harris hawks optimization (HHO) algorithm was used to attempt to solve the VRP. The algorithm was applied to 10 scenarios and the experimental results revealed that the HHO had a strong ability to check for and find the best route as compared to other metaheuristic algorithms, namely, simulated annealing and artificial bee colony optimization. The comparison was based on three criteria: minimum objective function obtained, minimum number of iterations required and satisfaction of capacity constraints. In all scenarios, the HHO showed clear superiority over the other methods.


2021 ◽  
Author(s):  
Mohamed Shams

Abstract This paper provides the field application of the bee colony optimization algorithm in assisting the history match of a real reservoir simulation model. Bee colony optimization algorithm is an optimization technique inspired by the natural optimization behavior shown by honeybees during searching for food. The way that honeybees search for food sources in the vicinity of their nest inspired computer science researchers to utilize and apply same principles to create optimization models and techniques. In this work the bee colony optimization mechanism is used as the optimization algorithm in the assisted the history matching workflow applied to a reservoir simulation model of WD-X field producing since 2004. The resultant history matched model is compared with with those obtained using one the most widely applied commercial AHM software tool. The results of this work indicate that using the bee colony algorithm as the optimization technique in the assisted history matching workflow provides noticeable enhancement in terms of match quality and time required to achieve a reasonable match.


Author(s):  
Shahab Wahhab Kareem ◽  
Shavan Askar ◽  
Roojwan Sc. Hawezi ◽  
Glena Aziz Qadir ◽  
Dina Yousif Mikhail

Swarm intelligence (SI), an important aspect of artificial intelligence, is increasingly gaining popularity as more and more high-complexity challenges necessitate solutions that are sub-optimal but still feasible in a fair amount of time. Artificial intelligence that mimics the collective behavior of a group of animals is known as swarm intelligence. Attempting to survive. It is primarily influenced by biological systems. The main aim of our article is to find out more about the guiding principle, classify possible implementation areas, and include a thorough analysis of several SI algorithms. Swarms can be observed in ant colonies, fish schools, bird flocks, among other fields. During this article, we will look at some Swarm instances and their behavior. We see many Swarm Intelligence systems, like Ant colony Optimization, which explains ant activity, nature, and how they conquer challenges; in birds, we see Particle Swarm Optimization is a swarm intelligence-based optimization technique, and how the locations must be positioned based on the three concepts. The Bee Colony Optimization follows, and explores the behavior of bees, their relationships, as well as movement and how they work in a swarm. This paper explores some of the methods and algorithms.


Author(s):  
L. S. Suma ◽  
S. S. Vinod Chandra

In this work, we have developed an optimization framework for digging out common structural patterns inherent in DNA binding proteins. A novel variant of the artificial bee colony optimization algorithm is proposed to improve the exploitation process. Experiments on four benchmark objective functions for different dimensions proved the speedier convergence of the algorithm. Also, it has generated optimum features of Helix Turn Helix structural pattern based on the objective function defined with occurrence count on secondary structure. The proposed algorithm outperformed the compared methods in convergence speed and the quality of generated motif features. The motif locations obtained using the derived common pattern are compared with the results of two other motif detection tools. 92% of tested proteins have produced matching locations with the results of the compared methods. The performance of the approach was analyzed with various measures and observed higher sensitivity, specificity and area under the curve values. A novel strategy for druggability finding by docking studies, targeting the motif locations is also discussed.


2021 ◽  
Author(s):  
Mohamed Shams ◽  
Ahmed El-Banbi ◽  
M. Helmy Sayyouh

Abstract Bee colony optimization technique is a stochastic population-based optimization algorithm inspired by the natural optimization behavior shown by honey bees during searching for food. Bee colony optimization algorithm has been successfully applied to various real-world optimization problems mostly in routing, transportation, and scheduling fields. This paper introduces the bee colony optimization method as the optimization technique in reservoir engineering assisted history matching procedure. The superiority of the proposed optimization algorithm is validated by comparing its performance with two other advanced nature-inspired optimization techniques (genetic and particle swarm optimization algorithms) in three synthetic assisted history matching problems. In addition, this paper presents the application of the bee colony optimization technique in assisting the history match of a full field reservoir simulation model of a mature gas-cap reservoir with 28 years of history. The resultant history matched model is compared with those obtained using a manual history matching procedure and using the most widely applied optimization algorithm used in assisted history matching commercial software tools. The results of this work indicate that employing the bee colony algorithm as the optimization technique in the assisted history matching workflow yields noticeable enhancement in terms of match quality and time required to achieve a reasonable match.


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