Abstract:<abstract>
<p>The balance between exploration and exploitation is critical to the performance of a Meta-heuristic optimization method. At different stages, a proper tradeoff between exploration and exploitation can drive the search process towards better performance. This paper develops a multi-objective grasshopper optimization algorithm (MOGOA) with a new proposed framework called the Multi-group and Co-evolution Framework which can archive a fine balance between exploration and exploitation. … Show more
“…Then, in order to solve the problem that the diversity measurement method selected in this paper cannot correspond to different dimensions of each dimension, a standard diversity measurement method is proposed in this paper. In addition, this paper also briefly introduces the commonly used diversity guidance strategy and multi-group strategy, and puts forward the diversity guidance strategy and multi-group parallel strategy for the following content [8]. The literature shows that the definition of data mining standards exists in incomplete and noisy data sets, and the model is expressed in language when extracting patterns that are effective, innovative, useful, and ultimately essentially understandable, and therefore can be used to describe data set subsets that require the model to be simpler than computing data subsets.…”
In the Internet era, enterprises are surrounded by massive information, and the information ability of effective organization is becoming more and more important for modern enterprise management. How to continuously dig into the economic data formed in the operation of enterprises, and then discover the intelligence with the effect of financial risk warning, has become an urgent problem to be dealt with. Sophisticated data mining techniques continue to evolve, creating a clear sense of direction for dealing with this problem. Like most collective intelligence algorithms, optimized particle swarm optimization is a typical algorithm for optimizing collective intelligence. Usually, a set of solutions is started randomly, but these solutions are also updated through continuous repetition. Therefore, this paper, aiming at this problem, has carried on a series of improvement methods, improved the particle property algorithm and analyzed the particle swarm optimization algorithm, at the same time, clarified its long time in the elimination stage and can not be applied to some problems. Therefore, how to design improved strategies for swarm intelligence algorithms, especially better particle walking algorithms and their variants, to further improve the solving quality and efficiency of such algorithms when solving complex optimization problems is also a research focus in this field.
“…Then, in order to solve the problem that the diversity measurement method selected in this paper cannot correspond to different dimensions of each dimension, a standard diversity measurement method is proposed in this paper. In addition, this paper also briefly introduces the commonly used diversity guidance strategy and multi-group strategy, and puts forward the diversity guidance strategy and multi-group parallel strategy for the following content [8]. The literature shows that the definition of data mining standards exists in incomplete and noisy data sets, and the model is expressed in language when extracting patterns that are effective, innovative, useful, and ultimately essentially understandable, and therefore can be used to describe data set subsets that require the model to be simpler than computing data subsets.…”
In the Internet era, enterprises are surrounded by massive information, and the information ability of effective organization is becoming more and more important for modern enterprise management. How to continuously dig into the economic data formed in the operation of enterprises, and then discover the intelligence with the effect of financial risk warning, has become an urgent problem to be dealt with. Sophisticated data mining techniques continue to evolve, creating a clear sense of direction for dealing with this problem. Like most collective intelligence algorithms, optimized particle swarm optimization is a typical algorithm for optimizing collective intelligence. Usually, a set of solutions is started randomly, but these solutions are also updated through continuous repetition. Therefore, this paper, aiming at this problem, has carried on a series of improvement methods, improved the particle property algorithm and analyzed the particle swarm optimization algorithm, at the same time, clarified its long time in the elimination stage and can not be applied to some problems. Therefore, how to design improved strategies for swarm intelligence algorithms, especially better particle walking algorithms and their variants, to further improve the solving quality and efficiency of such algorithms when solving complex optimization problems is also a research focus in this field.
“…In view of these shortcomings, the mechanism of Opposite Learning in SSA(OLSSA) was introduced 17 to extend the optimization range and prevent the method from falling into the trap of local optimum. In this work, the optimization capability of OLSSA is further improved by using the idea of population guidance 18 after considering the idea of opposite learning in the initialization of the algorithm.…”
Section: Population Guided Inversed Optimization Sparrow Search Algor...mentioning
In Software Defined Network (SDN), the control network is a logically mapped network that is like the brain and control center of the entire network. The reliability of the control network is the ability to survive failures of switching nodes on a large scale. However, there are few studies that consider the control network of SDN as a whole. In this work, we took the SDN control network as a standalone network that is separate from the underlying switching network. We developed a model to evaluate the average reliability of the control network in the presence of multiple failures of the underlying switching networks based on its topology characteristics. Then we searched for the optimal solutions of the model through an opposite learning revised sparrow search algorithm, based on which we proposed a multiple fault recovery oriented deployment strategy of backup controllers in SDN. The simulated experiment shows that the revision of the algorithm is effective and our proposed strategy could help us increase the reliability of the control network to a significant extent.
“…Grouping the population is an effective solution, and its intention is to increase the scope of the search space through grouping. Similarly, it is also mentioned in [59] to improve the performance of the algorithm by setting different parameters and search strategies for each population. It is worth noting that our grouping here is to make population 1 pay more attention to convergence and ensure the convergence of the algorithm.…”
Section: B Clustering Based On Indicatorsmentioning
Recently, the particle swarm algorithm (PSO) has demonstrated its effectiveness in solving multi-objective optimization problems. However, the performance of most existing multi-objective particle swarm algorithms depends largely on the global or individual best particles. Moreover, due to the rapid convergence of PSO in single objective optimization problems, PSO is prone to poorly distributed indicators when dealing with multi-objective optimization problems. To solve the above problems, we propose a multi-objective competitive particle swarm algorithm based on vector angles (VaCSO). Firstly, in order to remove the influence of global best particles or individual best particles on the algorithm, the competition mechanism is used. Secondly, in order to increase the diversity of solutions while maintaining the convergence of the algorithm, the population is clustered into two populations. Population 1 mainly considers the convergence of the solution in the offspring generation strategy. As a supplement, population 2 adds a new offspring generation strategy to maintain the distribution of the solution, and we innovatively proposed a three-particle competition to improve the distribution and diversity of particle swarms. Finally, based on vector angle information, we consider auxiliary learning to optimize the population gap, so as to improve the distribution of the algorithm. We have established two sets of comparative experiments to test the performance of VaCSO. We compared VaCSO with the currently popular multi-objective particle swarm optimizers and multi-objective evolutionary algorithms. Experimental results show that VaCSO has an excellent performance in convergence and distribution, and has a significant effect in optimizing quality.INDEX TERMS multi-objective optimization, competition particle swarm, competition mechanism, threeparticle competition, vector angle information, auxiliary learning.
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