A New Gaining-Sharing Knowledge Based Algorithm with Parallel Opposition-Based Learning for Internet of Vehicles

Question

1. What are the key parameters in the GSK algorithm and how do they influence the knowledge sharing process?

2. What is the Taguchi method?

3. What is the main idea behind Opposition-Based Learning (OBL)?

4. What is the importance of resource scheduling in IoV?

Accepted Answer

The GSK algorithm consists of two main phases: the junior phase and the senior phase. Key parameters in the GSK algorithm include knowledge rate (K), knowledge factor (Kf), knowledge ratio (Kr), and the proportion of the population (p). The knowledge rate (K) controls the proportion of junior and senior schemes in the individual renewal scheme, determining the balance between the junior gaining-sharing and senior gaining-sharing knowledge phases. The knowledge factor (Kf) controls the total amount of knowledge currently learned by the individual from others, influencing the rate at which knowledge is acquired. The knowledge ratio (Kr) controls the ratio between the current and acquired experience, affecting the individual's ability to distinguish right from wrong and interact with unfamiliar individuals. The proportion of the population (p) divides the population into three groups: the best, the worst, and the middle, influencing the sources of information for knowledge sharing. These parameters collectively shape the knowledge sharing process in the GSK algorithm, allowing for the gradual updating of knowledge and the acquisition of new insights.

Accepted Answer

The Taguchi method, developed by Dr. Genichi Taguchi in Japan after World War II, is a statistical approach used in engineering to design products with minimum cost and time. It aims to improve product quality by testing multiple factors simultaneously using orthogonal matrix experiments. This method reduces the number of experiments required, saving time and production costs. The orthogonal matrix experiment uses a pre-designed matrix to conduct a small number of experiments on each factor, achieving almost the same effect as testing one by one. The Taguchi method has greatly promoted Japan's economic recovery and is widely used in practical production.

Accepted Answer

The main idea behind Opposition-Based Learning (OBL) is to find the opposite position of the random initial population, evaluate it, and select the better position to replace the initial population. This approach modifies the convergence direction of the algorithm and improves the search accuracy. By evaluating the position of the current individual and its opposite, better individuals are left during the population updating process. OBL aims to get close to the optimal position quickly, even if the initial populations are randomly generated and far from the optimal solution. The Opposite Number and Opposite Point concepts are used to define the opposite position and coordinate system in the search process, respectively. These concepts help in dynamically changing the search space and finding better solutions.

Accepted Answer

Resource scheduling in IoV is crucial for distributing user workload efficiently among RSUs, ensuring optimal QoS, and improving computing capabilities. It considers resource capacity, over-and underutilization, and various performance indicators. Algorithms like SAGIN-IoV, big data-based optimization, and FR-MOS aim to enhance resource utilization, reduce costs, and balance load while considering practical usage scenarios and multiple objectives such as service delay, resource utilization, load balancing, and security. These algorithms are essential for the successful deployment of IoV architecture and intelligent transportation systems.

Accepted Answer

The primary communication strategy in parallel strategy involves two main approaches. The first is controlled by the communication control factor R, where if the random number generated is greater than R, all groups are matched in pairs and the Taguchi communication process is performed. This process includes selecting the optimal solution, conducting experiments based on the orthogonal table, and calculating the fitness value for each individual. If the random number is less than R, the optimal solutions within all groups are compared with the global optimal solution. If worse, the intra-group optimal solution is replaced by the global optimal solution, and if better, a random mutation operation is performed. The Taguchi method efficiently excavates excellent dimensions and combines them together. The second primary communication strategy is the population-merger communication strategy, where subpopulations search independently and merge into one population at specific conditions, incorporating all information from both subpopulations. This strategy aises diversity and search performance in parallel strategies.

Accepted Answer

OBL improves GSK optimization by generating a population closer to the optimal position. Initially, the population is randomly generated within a defined range, which may be too far from the global optimal position. By incorporating OBL, the initial population is optimized, and the opposite population is generated to correct the convergence direction. This ensures that the current population is closer to the global optimal position, facilitating more effective algorithm optimization. The steps involve initializing the population, generating the opposing population, selecting individuals with excellent fitness, and updating the population. The probability of generating an opposing population is controlled by adjusting the jump rate. In POGSK, the initial population is divided into subpopulations, and each subpopulation conducts GSK independently, communicating with each generation according to the Taguchi method. The pseudocode for POGSK is shown in Algorithm 4, and Figure 2 illustrates the main processes. Overall, OBL enhances GSK optimization by improving the population's proximity to the optimal position and ensuring effective algorithm optimization.

Accepted Answer

The POGSK model can enhance QoS in IoV by providing a mathematical framework for resource allocation among RSUs. It considers multiple vehicles, tasks, and processing nodes, taking into account task attributes such as size, deadlines, resource requirements, and transfer time. The model also factors in processing unit attributes like resource ownership and processing capacity. By calculating processing times and service delays, the POGSK model optimizes resource utilization and minimizes energy consumption. This leads to faster services, efficient resource allocation, and improved QoS in IoV.

Accepted Answer

Load balancing in cloud computing can enhance performance by evenly distributing service requests across different resources, preventing unbalanced loads. This technique improves accuracy and efficiency, ensuring optimal resource utilization. By calculating the mean and variance of resource utilization for each processing unit, load balancing can achieve a balanced resource allocation. The proposed fitness function in the IoV section considers resource constraints and task deadlines to optimize load balancing, ultimately improving cloud computing performance.

Accepted Answer

POGSK outperforms GSK, DE, GWO, and PSO in 26, 23, 25, and 25 test functions respectively. It achieves excellent results on 26 test functions, with five reaching the minimum value. POGSK also shows superior performance on Composition Functions (21-30), demonstrating strong search ability. The results are based on 31 independent experiments with a population size of 100 and a solution range of [-100, 100].

Accepted Answer

POGSK outperforms GSK, PSO, and DE in 26 out of 31 test functions, with five functions reaching the minimum value. It also shows excellent results on 28 functions compared to GWO and 25 functions compared to PSO. POGSK obtained 7 times better results on functions 21-30, validating its excellent search ability on combinatorial functions. Further experiments with different NFES termination conditions demonstrate POGSK's strong optimization performance, even when NFES is reduced. The convergence curves of POGSK on nine benchmark functions show its powerful search ability to effectively avoid local optima, highlighting the enhancement of the original algorithm with the addition of the OBL and parallel strategies. Overall, POGSK has better capability in the CEC2017 test suite compared to GSK, PSO, DE, and GWO.

Accepted Answer

POGSK achieves excellent results in 9 out of 11 experiments, winning against GSK, GWO, and PSO. The parallel strategy and OBL strategy contribute to its success. The Taguchi communication strategy helps GSK avoid local optima, while the population-merging strategy prevents POGSK from weakening due to reduced individuals. The OBL strategy corrects convergence direction and speeds up convergence. Figure 6 demonstrates POGSK's ability to avoid local optima and perform well in constrained realistic optimization problems.

Accepted Answer

The POGSK algorithm utilizes the original algorithm, OBL, and parallel strategy to address the resource-scheduling problem in IoV. It employs the Taguchi strategy and population merger strategy for information exchange among subpopulations. The algorithm demonstrates stronger searching ability compared to the original algorithm and classical algorithms tested on CEC2017. Simulation tests show improved results when applying POGSK to the resource-scheduling problem. Future improvements can focus on enhancing inter-group communication strategies and search capabilities. Additionally, the algorithm can be applied to multi-objective, engineering optimization, and binary optimization problems, potentially achieving better results.

A New Gaining-Sharing Knowledge Based Algorithm with Parallel Opposition-Based Learning for Internet of Vehicles

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Most frequently asked questions

1. What are the key parameters in the GSK algorithm and how do they influence the knowledge sharing process?

2. What is the Taguchi method?

3. What is the main idea behind Opposition-Based Learning (OBL)?

4. What is the importance of resource scheduling in IoV?

5. What is the primary communication strategy in parallel strategy?

6. How does OBL improve GSK optimization?

7. How can POGSK model enhance QoS in IoV?

8. How can load balancing enhance cloud computing performance?

9. How does POGSK compare to other algorithms in CEC2017?

10. How does POGSK compare to GSK, PSO, and DE?

11. How does POGSK perform in constrained realistic optimization problems?

12. What is the POGSK algorithm's approach to solving the resource-scheduling problem in IoV?

Citations

A Privacy-Preserving Authentication Protocol for Electric Vehicle Battery Swapping Based On Intelligent Blockchain

Application of Diversity-Maintaining Adaptive Rafflesia Optimization Algorithm to Engineering Optimisation Problems

DMT-OMPA: Innovative applications of an efficient adversarial Marine Predators Algorithm based on dynamic matrix transformation in engineering design optimization

References

Optimization by Simulated Annealing

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

A new optimizer using particle swarm theory

Grey Wolf Optimizer

On genetic algorithms

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