A Database Grid Service with a Novel Mutation Operator

2014 ◽  
Vol 989-994 ◽  
pp. 4869-4872
Author(s):  
Jian Cao ◽  
Yan Bin Li ◽  
Cong Yan
Keyword(s):  
Optimization Problems ◽  
Novel Mutation ◽  
Experimental Studies ◽  
Heterogeneous Databases ◽  
Grid Service ◽  
Benchmark Problems ◽  
Mutation Operator ◽  
Grid Portal ◽  
Complex Optimization ◽  
Improved Pso

Database grid service provides users with a unified interface to access to distributed heterogeneous databases resources. To overcome the weakness of collaborative services ability in different grid portal, a new grid portal architecture based on CSGPA (Collaborative Services Grid Portal Architecture), is proposed. This paper aims to enhance the performance of PSO in complex optimization problems and proposes an improved PSO variant by incorporating a novel mutation operator. Experimental studies on some well-known benchmark problems show that our approach achieves promising results.

Identical Distributed Pairwise NQD Random Variables Sequences with a Novel Mutation Operator

2014 ◽  
Vol 989-994 ◽  
pp. 2491-2494
Author(s):  
Jian Cao ◽  
Gang Li ◽  
Cen Rui Ma
Keyword(s):  
Optimization Problems ◽  
Novel Mutation ◽  
Random Variables ◽  
Strong Stability ◽  
Mutation Operator ◽  
Linear Forms ◽  
Grid Portal ◽  
Complex Optimization ◽  
Independent Identically Distributed ◽  
Improved Pso

We study the strong stability of linear forms of pairwise negatively quadrant dependent (NQD) identically distributed random variables sequence under some suitable conditions. To overcome the weakness of collaborative services ability in different grid portal, a new grid portal architecture based on CSGPA (Collaborative Services Grid Portal Architecture), is proposed. This paper aims to enhance the performance of PSO in complex optimization problems and proposes an improved PSO variant by incorporating a novel mutation operator. The results obtained extend and improve the corresponding theorem for independent identically distributed random variables sequence.

Data Warehouse Algorithm Study with a Novel Mutation Operator

2014 ◽  
Vol 602-605 ◽  
pp. 3388-3391
Author(s):  
Jian Cao ◽  
Gang Li ◽  
Cheng Tao Zhang
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Optimization Problems ◽  
Novel Mutation ◽  
Random Variables ◽  
Current Situation ◽  
Plan Recognition ◽  
Mutation Operator ◽  
Complex Optimization ◽  
Independent Identically Distributed

Focused on the issue of establishing data warehouse in data mining study, the study also considered the current situation of researches into dynamic plan recognition. This paper aims to enhance the performance of PSO in complex optimization problems and proposes an improved PSO variant by incorporating a novel mutation operator. The results obtained extend and improve the corresponding theorem for independent identically distributed random variables sequence.

scholarly journals Disruption-Based Multiobjective Equilibrium Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Novel Mutation ◽  
Optimization Algorithms ◽  
Benchmark Problems ◽  
Engineering Optimization ◽  
Test Results ◽  
Nature Inspired Computing ◽  
Disruption Method

Nature-inspired computing has attracted huge attention since its origin, especially in the field of multiobjective optimization. This paper proposes a disruption-based multiobjective equilibrium optimization algorithm (DMOEOA). A novel mutation operator named layered disruption method is integrated into the proposed algorithm with the aim of enhancing the exploration and exploitation abilities of DMOEOA. To demonstrate the advantages of the proposed algorithm, various benchmarks have been selected with five different multiobjective optimization algorithms. The test results indicate that DMOEOA does exhibit better performances in these problems with a better balance between convergence and distribution. In addition, the new proposed algorithm is applied to the structural optimization of an elastic truss with the other five existing multiobjective optimization algorithms. The obtained results demonstrate that DMOEOA is not only an algorithm with good performance for benchmark problems but is also expected to have a wide application in real-world engineering optimization problems.

scholarly journals The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
S. Salcedo-Sanz ◽  
J. Del Ser ◽  
I. Landa-Torres ◽  
S. Gil-López ◽  
J. A. Portilla-Figueras
Keyword(s):  
Coral Reefs ◽  
Wind Farm ◽  
Optimization Problems ◽  
Mobile Network ◽  
Benchmark Problems ◽  
Wind Farm Design ◽  
Coral Reefs Optimization Algorithm ◽  
Open Line ◽  
Complex Optimization ◽  
Real World Problems

This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems.

A Database Grid Service Based on CSGPA

2014 ◽  
Vol 543-547 ◽  
pp. 3419-3422
Author(s):  
Jian Cao ◽  
Cong Yan
Keyword(s):  
Heterogeneous Databases ◽  
Grid Service ◽  
Grid Environment ◽  
Grid Portal

Database grid service provides users with a unified interface to access to distributed heterogeneous databases resources. To overcome the weakness of collaborative services ability in different grid portal, a new grid portal architecture based on CSGPA (Collaborative Services Grid Portal Architecture), is proposed. It devotes integrating database into Grid environment with grid service. In comparison with the current mainstream grid portal architecture, the results show that CSGPA has great advantage in efficiency, deployment costs, scalability and reusability etc.

scholarly journals Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm

2015 ◽  
Vol 3 (1) ◽  
pp. 24-36 ◽  
Author(s):  
Maziar Yazdani ◽  
Fariborz Jolai
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Population Based ◽  
Metaheuristic Algorithms ◽  
Benchmark Problems ◽  
The Past ◽  
Complex Optimization ◽  
Lion Optimization Algorithm ◽  
Population Based Algorithm

Abstract During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. In this paper, a new population based algorithm, the Lion Optimization Algorithm (LOA), is introduced. Special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm. Some benchmark problems are selected from the literature, and the solution of the proposed algorithm has been compared with those of some well-known and newest meta-heuristics for these problems. The obtained results confirm the high performance of the proposed algorithm in comparison to the other algorithms used in this paper.

Graph Partitioning Based on the Combined Approach

2020 ◽  
Vol 26 (12) ◽  
pp. 667-672
Author(s):  
V. V. Kureichik ◽  
◽  
Vl. Vl. Kureichik ◽  
Keyword(s):  
Graph Partitioning ◽  
Optimization Problems ◽  
Experimental Studies ◽  
Optimization Method ◽  
Combined Approach ◽  
Software Module ◽  
Combinatorial Optimization Problems ◽  
Partitioning Problem ◽  
Complex Optimization ◽  
Combined Algorithm

The article considers one of the most important combinatorial optimization problems — the problem of graph partitioning. It belongs to the class of NP-complex optimization problems. The article presents the partitioning problem statement. Due to the complexity of this task, the article proposes a new search strategy based on a combined approach. The combined approach is to divide the decision-making process into two levels. At the first level, the bee optimization method is used to quickly obtain subdomains with a high value of the objective function, and at the second level, an evolutionary algorithm is used to improve obtained solutions. To implement this approach, the authors developed a combined algorithm that can obtain sets of quasi-optimal solutions in polynomial time and avoid looping in local regions at the same time. A software module is developed and algorithms for partitioning graphs into parts are implemented. A computational experiment has been carried out when dividing into 8 parts of test circuits (benchmarks) by IBM. An analysis of experimental studies showed that the developed combined algorithm is on average 5 % higher than the partition results obtained by well-known hMetis, PGAComplex algorithms with comparable solution time, which indicates the effectiveness of the proposed approach. The time complexity of the developed combined algorithm is approximately O (n2).

scholarly journals A Fusion Multiobjective Empire Split Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Liang Liang
Keyword(s):  
Multiobjective Optimization ◽  
Learning Strategy ◽  
Optimization Problems ◽  
Experimental Studies ◽  
Imperialist Competitive Algorithm ◽  
Individual Performance ◽  
Benchmark Problems ◽  
Initial Population ◽  
Opposition Based Learning ◽  
Multiobjective Optimization Problems

In the last two decades, swarm intelligence optimization algorithms have been widely studied and applied to multiobjective optimization problems. In multiobjective optimization, reproduction operations and the balance of convergence and diversity are two crucial issues. Imperialist competitive algorithm (ICA) and sine cosine algorithm (SCA) are two potential algorithms for handling single-objective optimization problems, but the research of them in multiobjective optimization is scarce. In this paper, a fusion multiobjective empire split algorithm (FMOESA) is proposed. First, an initialization operation based on opposition-based learning strategy is hired to generate a good initial population. A new reproduction of offspring is introduced, which combines ICA and SCA. Besides, a novel power evaluation mechanism is proposed to identify individual performance, which takes into account both convergence and diversity of population. Experimental studies on several benchmark problems show that FMOESA is competitive compared with the state-of-the-art algorithms. Given both good performance and nice properties, the proposed algorithm could be an alternative tool when dealing with multiobjective optimization problems.

scholarly journals Quantum Behaved Particle Swarm Optimization with Neighborhood Search for Numerical Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xiao Fu ◽  
Wangsheng Liu ◽  
Bin Zhang ◽  
Hua Deng
Keyword(s):  
Particle Swarm Optimization ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
State Of The Art ◽  
Experimental Studies ◽  
Particle Swarm ◽  
Neighborhood Search ◽  
Swarm Optimization ◽  
Opposition Based Learning ◽  
Complex Optimization

Quantum-behaved particle swarm optimization (QPSO) algorithm is a new PSO variant, which outperforms the original PSO in search ability but has fewer control parameters. However, QPSO as well as PSO still suffers from premature convergence in solving complex optimization problems. The main reason is that new particles in QPSO are generated around the weighted attractors of previous best particles and the global best particle. This may result in attracting too fast. To tackle this problem, this paper proposes a new QPSO algorithm called NQPSO, in which one local and one global neighborhood search strategies are utilized to balance exploitation and exploration. Moreover, a concept of opposition-based learning (OBL) is employed for population initialization. Experimental studies are conducted on a set of well-known benchmark functions including multimodal and rotated problems. Computational results show that our approach outperforms some similar QPSO algorithms and five other state-of-the-art PSO variants.

scholarly journals Comparison between genetic algorithm and prey-predator algorithm

2014 ◽  
Vol 9 (4) ◽  
Author(s):  
Surafel Luleseged Tilahun ◽  
Hong Choon Ong
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Metaheuristic Algorithms ◽  
Benchmark Problems ◽  
Test Problems ◽  
Search Mechanism ◽  
Complex Optimization ◽  
Better Than

Metaheuristic algorithms are useful in solving complex optimization problems. Genetic algorithm (GA) is one of the well known and oldest metaheuristic algorithms. It was introduced in 1975 and has been used in many applications varying from engineering to management and many other fields as well. However, Prey-Predator algorithm (PPA) is one of recently introduced algorithm, in 2012, inspired by the interaction between preys and their predator. The motivation and the search mechanism for these two algorithms are different. In this paper the comparison of these two algorithms both from theoretical aspects and using simulation on selected benchmark problems is presented. According to the results, PPA performs better than GA in the selected test problems.

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