A Hybrid Optimization Algorithm based on Genetic Algorithm and Ant Colony Optimization

Zukhri, Zainudin; Paputungan, Irving Vitra

doi:10.5121/ijaia.2013.4505

Cited by 24 publications

(16 citation statements)

References 19 publications

(19 reference statements)

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“…The authors consider path representation method which simply lists the label of nodes. For example, let "1, 2, 3, 4, 5, 6, 7" be the labels of nodes in a seven node instance, then a tour "1→ 4→ 3→ 5→ 7→ 6→ 2→1" may be represented as (1,4,3,5,7,6,2). …”

Section: Genetic Representation and Codingmentioning

confidence: 99%

“…However, the computing time of their GA increases slowly with the growth of problem size, and for instances with more than 50 customers, the algorithm achieved optimal solution in less time than the incomplete optimization algorithm. A hybrid combination of genetic algorithm and ant colony is used to solve DTSP [10] and TSP [6] respectively. A simple technique for eliminating the redundant computations of GA and GA-based algorithms based on the notion of pattern reduction was presented in [32].…”

Section: Related Workmentioning

confidence: 99%

“…The list of the state capitals is given in Table 2 with the abbreviation. In the case study, unique identification numbers (ranging from one to seventeen) are used to represent each state capital in alphabetical order as follows: (1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17), these numbers are arranged in different order randomly to generate several tours. The tours that were generated are then put together and termed the "initial population."…”

Section: Proposed Hybrid Modelmentioning

confidence: 99%

“…Genetic Algorithm (GA) is a computational method designed to simulate the evolution processes and natural selection in organism [27], which follows the sequence as generating the initial population, evaluation, selection, crossover, mutation, and regeneration [6]. Genetic algorithm (GA) is an adaptive search technique which is based on the principles and mechanisms of natural selection and the survival of the fittest idea of natural evolution.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…The exact algorithm has high computational time while approximate method, based on heuristics, is more suitable. These set of algorithms had found solutions in TSP [6,7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Towards Solving Travelling Salesperson Problem using Hybrid of Genetic Algorithm and Lin-Kernighan Algorithm: A Comparative Evaluation with Neural Network Model

Oluwadare¹,

Ogunsanmi²,

Nwaiwu³

2017

IJCA

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Travelling salesperson problem involves the sales person who intends to find the minimum or shortest round trip that passes through a finite set of cities, exactly once at minimum cost. This problem belongs to the class of optimization problems which is described as non-deterministic polynomial hard, that is, it cannot be solved in exact polynomial time. Several approaches have been employed in solving the problem, but empirical results has shown that these approaches needs more optimization in terms of run time and quality of getting the optimum solution. Genetic algorithm combined with another local search algorithm shows more efficient result could be obtained. In this paper, hybridization of genetic algorithm with a local search algorithm called Lin-Kernighan algorithm is employed to provide efficient solution. A case study of finding the optimal solution for a tour of state capitals in Southern Nigeria is carried out. The model is implemented on Intel Celeron 2GHz, 1GB RAM machine with JAVA programming language and Wamp sever. The performance of the proposed hybrid genetic algorithm-based model is compared with Artificial Neural Network. The results showed that the proposed model performs better than neural network in terms of run-time and minimal tour distance.

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Section: Genetic Representation and Codingmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Proposed Hybrid Modelmentioning

confidence: 99%

Section: Genetic Algorithmmentioning

confidence: 99%

“…The exact algorithm has high computational time while approximate method, based on heuristics, is more suitable. These set of algorithms had found solutions in TSP [6,7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Towards Solving Travelling Salesperson Problem using Hybrid of Genetic Algorithm and Lin-Kernighan Algorithm: A Comparative Evaluation with Neural Network Model

Oluwadare¹,

Ogunsanmi²,

Nwaiwu³

2017

IJCA

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Join query optimization in the distributed database system using an artificial bee colony algorithm and genetic operators

Panahi

Navimipour

2019

Concurrency and Computation

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As the main factor in the distributed database systems, query optimization is aimed at finding an optimal execution plan to reduce the runtime. In such systems, because of the repeated relations on various sites, the query optimization is very challenging. Moreover, the query optimization issue with large-scale distributed databases is an NP-hard problem. Therefore, in this paper, an Artificial Bee Colony Algorithm based on Genetic Operators (ABC-GO) is proposed to find a solution to join the query optimization problems in the distributed database systems. The ABC algorithm has the global-local search capabilities and genetic operators to create new candidate solutions for improving the performance of the ABC algorithm. The obtained results have shown that the cost of the query evaluation is minimized and the quality of Top-K query plans is improved for a given distributed query. Moreover, this method decreases the overhead.However, it needs a longer execution time. KEYWORDSartificial bee colony optimization, distributed database, genetic operator, query plan optimization INTRODUCTIONDue to the rapid growth of data in current systems, 1,2 a set of the interrelated databases, known as a distributed database, is stored on multiple computers to improve performance, reliability, accessibility, and modularity in such cases compared to the conventional centralized database system. 3-7 In today's world, every user of the system, whether an employee or a customer, needs access to the company's databases. The data in distributed databases may be repeated at different sites according to a distributed allocation plan. 8-10 As a result, an association among different sites is needed to create answers to a user query. 11 The query optimization is a critical subject for the distributed database management system. Therefore, the poor performance of database for answering the query is the result of poor selection of a query execution plan. 12The query is a statement or a group of statements that performs some simple database operations such as deletion, updating, and retrieving data. It plays a critical role in data management and retrieval. Commonly, the distributed queries are more complicated than centralized queries due to data distribution over different sites. 13 Consequently, the impact of efficient query processing is increasing in a large number of applications.A common method for identifying the Top-k objects is that all database objects are scored on some function. Therefore, the DQP (distributed query process) strategy aims to generate the query processing plans that reduce the amount of data transmission between sites and, as a result, reduces the response time of the distributed query. The query optimization is a critical subject for database management systems, 14 whose purpose is to define an appropriate execution plan for the user's query. 15 The cost of each plan should be produced due to more than one plan for such a query, which is significantly dependent on the amount of participation and data transfer betwe...

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An energy‐aware approach for resource managing in the fog‐based Internet of Things using a hybrid algorithm

Ren

Zhang

Arefzadeh

2020

Int J Communication

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SummaryRegarding the recent information technology improvement, the fog computing (FC) emergence increases the ability of computational equipment and supplies modern solutions for traditional industrial applications. In the fog environment, Internet of Things (IoT) applications are completed by computing nodes that are intermediate in the fog, and the physical servers in data centers of the cloud. From the other side, because of resource constraints, dynamic nature, resource heterogeneity, and volatility of fog environment, resource management problems must be considered as one of the challenging issues of fog. The resource managing problem is an NP‐hard issue, so, in the current article, a powerful hybrid algorithm for managing resources in FC‐based IoT is proposed using an ant colony optimization (ACO) and a genetic algorithm (GA). GAs are computationally costly because of some problems such as the lack of guarantee for obtaining optimal solutions. Then, the precision and speed of convergence can be optimized by the ACO algorithm. Therefore, the powerful affirmative feedback pros of ACO on the convergence rate is considered. The algorithm uses GA's universal investigation power, and then it is transformed into ACO primary pheromone. This algorithm outperforms ACO and GA under equal conditions, as the simulation experiments showed.

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A Hybrid Optimization Algorithm based on Genetic Algorithm and Ant Colony Optimization

Abstract: In optimization problem, Genetic Algorithm (GA)

Cited by 24 publications

References 19 publications

Towards Solving Travelling Salesperson Problem using Hybrid of Genetic Algorithm and Lin-Kernighan Algorithm: A Comparative Evaluation with Neural Network Model

Towards Solving Travelling Salesperson Problem using Hybrid of Genetic Algorithm and Lin-Kernighan Algorithm: A Comparative Evaluation with Neural Network Model

Join query optimization in the distributed database system using an artificial bee colony algorithm and genetic operators

An energy‐aware approach for resource managing in the fog‐based Internet of Things using a hybrid algorithm

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