scholarly journals An Optimization Algorithm for Multipath Parallel Allocation for Service Resource in the Simulation Task Workflow

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Zhiteng Wang ◽  
Hongjun Zhang ◽  
Rui Zhang ◽  
Yong Li ◽  
Xuliang Zhang
Keyword(s):  
Modeling And Simulation ◽  
Optimization Algorithm ◽  
Search Space ◽  
Allocation Model ◽  
Coding Scheme ◽  
Probability Of Success ◽  
Service Resource ◽  
Service Oriented ◽  
Quantum Optimization ◽  
Simulation Task

Service oriented modeling and simulation are hot issues in the field of modeling and simulation, and there is need to call service resources when simulation task workflow is running. How to optimize the service resource allocation to ensure that the task is complete effectively is an important issue in this area. In military modeling and simulation field, it is important to improve the probability of success and timeliness in simulation task workflow. Therefore, this paper proposes an optimization algorithm for multipath service resource parallel allocation, in which multipath service resource parallel allocation model is built and multiple chains coding scheme quantum optimization algorithm is used for optimization and solution. The multiple chains coding scheme quantum optimization algorithm is to extend parallel search space to improve search efficiency. Through the simulation experiment, this paper investigates the effect for the probability of success in simulation task workflow from different optimization algorithm, service allocation strategy, and path number, and the simulation result shows that the optimization algorithm for multipath service resource parallel allocation is an effective method to improve the probability of success and timeliness in simulation task workflow.

scholarly journals Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm

2021 ◽  
Author(s):  
Prachi Agrawal ◽  
Talari Ganesh ◽  
Ali Wagdy Mohamed
Keyword(s):  
Life Span ◽  
Population Size ◽  
Linear Function ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Knapsack Problems ◽  
Local Optima ◽  
Knowledge Based ◽  
Two Stages

AbstractThis article proposes a novel binary version of recently developed Gaining Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. A binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (NBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable NBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Moreover, to enhance the performance of NBGSK and prevent the solutions from trapping into local optima, NBGSK with population size reduction (PR-NBGSK) is introduced. It decreases the population size gradually with a linear function. The proposed NBGSK and PR-NBGSK applied to set of knapsack instances with small and large dimensions, which shows that NBGSK and PR-NBGSK are more efficient and effective in terms of convergence, robustness, and accuracy.

Whale optimization algorithm based on lateral inhibition for image matching and vision-guided AUV docking

2020 ◽  
pp. 1-12
Author(s):  
Zheping Yan ◽  
Jinzhong Zhang ◽  
Jialing Tang
Keyword(s):  
Lateral Inhibition ◽  
Optimization Algorithm ◽  
Image Matching ◽  
Autonomous Underwater Vehicle ◽  
Optimal Solution ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Inhibition Mechanism ◽  
Whale Optimization ◽  
Accuracy And Stability

The accuracy and stability of relative pose estimation of an autonomous underwater vehicle (AUV) and a target depend on whether the characteristics of the underwater image can be accurately and quickly extracted. In this paper, a whale optimization algorithm (WOA) based on lateral inhibition (LI) is proposed to solve the image matching and vision-guided AUV docking problem. The proposed method is named the LI-WOA. The WOA is motivated by the behavior of humpback whales, and it mainly imitates encircling prey, bubble-net attacking and searching for prey to obtain the globally optimal solution in the search space. The WOA not only balances exploration and exploitation but also has a faster convergence speed, higher calculation accuracy and stronger robustness than other approaches. The lateral inhibition mechanism can effectively perform image enhancement and image edge extraction to improve the accuracy and stability of image matching. The LI-WOA combines the optimization efficiency of the WOA and the matching accuracy of the LI mechanism to improve convergence accuracy and the correct matching rate. To verify its effectiveness and feasibility, the WOA is compared with other algorithms by maximizing the similarity between the original image and the template image. The experimental results show that the LI-WOA has a better average value, a higher correct rate, less execution time and stronger robustness than other algorithms. The LI-WOA is an effective and stable method for solving the image matching and vision-guided AUV docking problem.

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

A Novel Discrete Binary Gaining-Sharing knowledge-based Optimization Algorithm for the Travelling Counselling Problem for Utilization of Solar Energy

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Solar Energy ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Knowledge Based ◽  
Power Stations ◽  
Binary Model ◽  
Application Problem ◽  
Counselling Process ◽  
Two Stages

This article proposes a novel binary version of recently developed Gaining-Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems is proposed. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. Discrete Binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (DBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable DBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space.An improved scheduling of the technical counselling process for utilization of the electricity from solar energy power stations is introduced. The scheduling aims at achieving the best utilization of the available day time for the counselling group,n this regard,a new application problem is presented, which is called a Travelling Counselling Problem (TCP).A Nonlinear Binary Model is introduced with a real application

Service-Oriented Cost Allocation for Business Intelligence and Analytics

2017 ◽  
Vol 7 (2) ◽  
pp. 40-57 ◽  
Author(s):  
Raphael Grytz ◽  
Artus Krohn-Grimberghe
Keyword(s):  
Business Intelligence ◽  
Cost Estimation ◽  
Cost Allocation ◽  
Cost Evaluation ◽  
Allocation Model ◽  
Pricing Scheme ◽  
Service Portfolio ◽  
Service Oriented ◽  
The Cost ◽  
Cost Awareness

Quantifying and designing the cost pool generated by Business Intelligence and Analytics (BI&A) would improve cost transparency and invoicing processes, allowing a fairer, more exact allocation of costs to service consumers. Yet there is still no method for determining BI&A costs to provide a base for allocation purposes. While literature describes several methods for BI&A cost estimation on an ROI or resource-consumption level, none of these methods considers an overall approach for BI&A. To tackle this problem, the authors propose a service- oriented cost allocation model which calculates BI&A applications based on defined services, enabling a cost transfer to service consumers. This new approach specifies steps towards deriving a usable pricing scheme for an entire BI&A service portfolio – both for allocation purposes as well as improving cost evaluation of BI&A projects. Moreover, it increases customer understanding and cost awareness. Based on this approach, the authors introduce a BI&A value creation cycle which helps customers to use BI&A services cost-effectively.

scholarly journals Achieving large and distant ancestral genome inference by using an improved discrete quantum-behaved particle swarm optimization algorithm

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhaojuan Zhang ◽  
Wanliang Wang ◽  
Ruofan Xia ◽  
Gaofeng Pan ◽  
Jiandong Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Experimental Results ◽  
The Other ◽  
Ancestral Genome ◽  
Swarm Optimization ◽  
Median Approach

Abstract Background Reconstructing ancestral genomes is one of the central problems presented in genome rearrangement analysis since finding the most likely true ancestor is of significant importance in phylogenetic reconstruction. Large scale genome rearrangements can provide essential insights into evolutionary processes. However, when the genomes are large and distant, classical median solvers have failed to adequately address these challenges due to the exponential increase of the search space. Consequently, solving ancestral genome inference problems constitutes a task of paramount importance that continues to challenge the current methods used in this area, whose difficulty is further increased by the ongoing rapid accumulation of whole-genome data. Results In response to these challenges, we provide two contributions for ancestral genome inference. First, an improved discrete quantum-behaved particle swarm optimization algorithm (IDQPSO) by averaging two of the fitness values is proposed to address the discrete search space. Second, we incorporate DCJ sorting into the IDQPSO (IDQPSO-Median). In comparison with the other methods, when the genomes are large and distant, IDQPSO-Median has the lowest median score, the highest adjacency accuracy, and the closest distance to the true ancestor. In addition, we have integrated our IDQPSO-Median approach with the GRAPPA framework. Our experiments show that this new phylogenetic method is very accurate and effective by using IDQPSO-Median. Conclusions Our experimental results demonstrate the advantages of IDQPSO-Median approach over the other methods when the genomes are large and distant. When our experimental results are evaluated in a comprehensive manner, it is clear that the IDQPSO-Median approach we propose achieves better scalability compared to existing algorithms. Moreover, our experimental results by using simulated and real datasets confirm that the IDQPSO-Median, when integrated with the GRAPPA framework, outperforms other heuristics in terms of accuracy, while also continuing to infer phylogenies that were equivalent or close to the true trees within 5 days of computation, which is far beyond the difficulty level that can be handled by GRAPPA.

scholarly journals Insights into Exploration and Exploitation Power of Optimization Algorithm Using DSCTool

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1474
Author(s):  
Peter Korošec ◽  
Tome Eftimov
Keyword(s):  
Statistical Analysis ◽  
Web Service ◽  
Optimization Algorithm ◽  
Optimization Algorithms ◽  
Search Space ◽  
Exploration And Exploitation ◽  
Learning Tool ◽  
Additional Information ◽  
E Learning ◽  
Single Objective

When making statistical analysis of single-objective optimization algorithms’ performance, researchers usually estimate it according to the obtained optimization results in the form of minimal/maximal values. Though this is a good indicator about the performance of the algorithm, it does not provide any information about the reasons why it happens. One possibility to get additional information about the performance of the algorithms is to study their exploration and exploitation abilities. In this paper, we present an easy-to-use step by step pipeline that can be used for performing exploration and exploitation analysis of single-objective optimization algorithms. The pipeline is based on a web-service-based e-Learning tool called DSCTool, which can be used for making statistical analysis not only with regard to the obtained solution values but also with regard to the distribution of the solutions in the search space. Its usage does not require any special statistic knowledge from the user. The gained knowledge from such analysis can be used to better understand algorithm’s performance when compared to other algorithms or while performing hyperparameter tuning.

scholarly journals Random search with k -prototypes algorithm for clustering mixed datasets

2011 ◽  
Vol 467 (2132) ◽  
pp. 2387-2403 ◽  
Author(s):  
Duc-Truong Pham ◽  
Maria M. Suarez-Alvarez ◽  
Yuriy I. Prostov
Keyword(s):  
Optimization Algorithm ◽  
Random Search ◽  
Search Space ◽  
Population Based ◽  
The Other ◽  
Bees Algorithm ◽  
Neighbourhood Search ◽  
Promising Solution ◽  
Number Of Iterations ◽  
Selection Of

A new algorithm to cluster datasets with mixed numerical and categorical values is presented. The algorithm, called RANKPRO (random search with k -prototypes algorithm), combines the advantages of a recently introduced population-based optimization algorithm called the bees algorithm (BA) and k -prototypes algorithm. The BA works with elite and good solutions, and continues to look for other possible extrema solutions keeping the number of testing points constant. However, the improvement of promising solutions by the BA may be time-consuming because it is based on random neighbourhood search. On the other hand, an application of the k -prototypes algorithm to a promising solution may be very effective because it improves the solution at each iteration. The RANKPRO algorithm balances two objectives: it explores the search space effectively owing to random selection of new solutions, and improves promising solutions fast owing to employment of the k -prototypes algorithm. The efficiency of the new algorithm is demonstrated by clustering several datasets. It is shown that in the majority of the considered datasets when the average number of iterations that the k -prototypes algorithm needs to converge is over 10, the RANKPRO algorithm is more efficient than the k -prototypes algorithm.

scholarly journals Multi-Auv Distributed Task Allocation Based on the Differential Evolution Quantum Bee Colony Optimization Algorithm

2017 ◽  
Vol 24 (s3) ◽  
pp. 65-71
Author(s):  
Jianjun Li ◽  
Ru Bo Zhang
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Task Allocation ◽  
Artificial Bee Colony ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Allocation Model ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Distributed Task Allocation

Abstract The multi-autonomous underwater vehicle (AUV) distributed task allocation model of a contract net, which introduces an equilibrium coefficient, has been established to solve the multi-AUV distributed task allocation problem. A differential evolution quantum artificial bee colony (DEQABC) optimization algorithm is proposed to solve the multi-AUV optimal task allocation scheme. The algorithm is based on the quantum artificial bee colony algorithm, and it takes advantage of the characteristics of the differential evolution algorithm. This algorithm can remember the individual optimal solution in the population evolution and internal information sharing in groups and obtain the optimal solution through competition and cooperation among individuals in a population. Finally, a simulation experiment was performed to evaluate the distributed task allocation performance of the differential evolution quantum bee colony optimization algorithm. The simulation results demonstrate that the DEQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The DEQABC algorithm can effectively improve AUV distributed multi-tasking performance.

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