Particle Swarm Optimization with Mutation for High Dimensional Problems

Achtnig, Jeff

doi:10.1007/978-3-540-75396-4_15

Cited by 6 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the proposed method was tested on the standard benchmark functions in order to be compared with the PSO and BFO algorithms, the results of which indicated the proposed method's capability to improve the convergence rate and optimization precision. In [7], Achting used PSO in order to solve the highdimensional optimization problems. The obtained results showed that a combination of the PSO algorithms with some concepts of the evolutionary algorithm, such as mutation operator, would significantly improve the PSO algorithm's performance in solving the high-dimensional optimization problems.…”

Section: Related Workmentioning

confidence: 99%

“…The metaheuristic algorithms are considered among the most promising methods for solving the optimization problems. The investigations and studies by several researchers on metaheuristic algorithms have yielded various nature-inspired and non-`e-inspired algorithms, some of which include: GA [6], PSO [7], ABC [8], FA [9], BA [10], GWO [11], WOA [12], and FFA [4], etc. The main advantage of these algorithms is the use of the "trial and error" principle in searching for the solutions, as a result of which they have been used successfully for solving the global optimization problems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Multi-Agent Approach for Solving Optimization Problems with High-Dimensional: Case Study in Email Spam Detection

Mohmmadzadeh¹,

Gharehchopogh²

2020

Preprint

View full text Add to dashboard Cite

There exist numerous high-dimensional problems in the real world which cannot be solved through the common traditional methods. The metaheuristic algorithms have been developed as successful techniques for solving a variety of complex and difficult optimization problems. Notwithstanding their advantages, these algorithms may turn out to have weak points such as lower population diversity and lower convergence rate when facing complex high-dimensional problems. An appropriate approach to solve such problems is to apply multi-agent systems along with the metaheuristic algorithms. The present paper proposes a new approach based on the multi-agent systems and the concept of agent, which is named Multi-Agent Metaheuristic (MAMH) method. In the proposed approach, several basic and powerful metaheuristic algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Crow Search Algorithm (CSA), Farmland Fertility Algorithm (FFA), are considered as separate agents each of which sought to achieve its own goals while competing and cooperating with others to achieve the common goals. In overall, the proposed method was tested on 32 complex benchmark functions, the results of which indicated effectiveness and powerfulness of the proposed method for solving the high-dimensional optimization problems. In addition, in this paper, the binary version of the proposed approach, called Binary MAMH (BMAMH), was executed on the spam email dataset. According to the results, the proposed method exhibited a higher precision in detection of the spam emails compared to other metaheuristic algorithms and methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Multi-Agent Approach for Solving Optimization Problems with High-Dimensional: Case Study in Email Spam Detection

Mohmmadzadeh¹,

Gharehchopogh²

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…At each iteration, particle i is randomly selected from the whole swarm uniformly, and dimension j from 1 to d as well. And then vij mutates as following [12,20,24]…”

Section: Si(t)= Xi(t) M=f(xi(t))mentioning

confidence: 99%

“…There have been many improved PSO algorithm while few modifications of PSO for high-dimensional multimodal functions [16][17][18][19][20]. Moreover, Most of them in the performance are not satisfactory [16][17][18][19] due to tremendous amount of local optimization with high-dimensional multimodal functions.…”

Section: Zhihua Cui Complex System and Computationalmentioning

confidence: 99%

Neighborhood sharing particle swarm optimization

Chu

Cui

2009

2009 8th IEEE International Conference on Cognitive Informatics

View full text Add to dashboard Cite

Biological results suggest that information provided by neighborhood of each individual offers an evolutionary advantage, furthermore, the current state of neighbors significantly impact on the decision process of group members. However, particle swarm algorithm, as a simulation of group foraging behavior, does not introduce the neighborhood sharing information into its evolutionary equations. Hence, this paper replaces the individual experience by the neighbor sharing information ofcurrent state and proposes the neighborhood sharing particle swarm algorithm In order to verify the performance of the algorithm, five typical high dimensional multimodal functions are selected and the simulation results show that the proposed algorithm is not only superior to the standard version, but also much better than the other two variants.Index Terms-Multimodal test functions, neighborhood, neighborhood sharing particle swarm optimization (NSPSO), particle swarm optimization (PSO), information sharing mechanism.

show abstract

“…The majority of global optimization algorithms lose the power of searching the optimal solution when the dimension increases. Therefore, more efficient search strategies are required to explore all the promising regions in a given time budget [17], [18] .…”

Section: Introductionmentioning

confidence: 99%

Multilevel Collaborative Aerodynamic Design Optimization Based on Sobol’ Global Sensitivity Analysis

Wang

Gao

2016

17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

View full text Add to dashboard Cite

Surrogate model combined with global optimization algorithm is necessary for design space exploration in aerodynamic shape optimization (ASO). However, the "curse of dimensionality" exists to a great extent in those global optimization algorithms. Multilevel collaborative optimization (MCO) method is studies to cope with high-dimensional optimization problems in this paper. The superiority of MCO method over traditional direct full-variables optimization method is confirmed through different test functions. In aerodynamic shape optimization, Sobol' global sensitivity analysis is introduced to quantify the importance degrees of design variables. The design variables are divided in to subcomponents according to their importance degrees and the subcomponents are optimized individually in multiple cycles. The MCO aerodynamic design framework is established by integrating the Sobol' global sensitivity analysis method, efficient shape parameterization method, mesh deformation technique, numerical simulation method and surrogate-based global optimizer. Finally, a commercial airplane in transonic regime is optimized by MCO method and conventional method respectively. Results show that the proposed MCO method is better than conventional method.

show abstract

Particle Swarm Optimization with Mutation for High Dimensional Problems

Cited by 6 publications

References 16 publications

A New Multi-Agent Approach for Solving Optimization Problems with High-Dimensional: Case Study in Email Spam Detection

A New Multi-Agent Approach for Solving Optimization Problems with High-Dimensional: Case Study in Email Spam Detection

Neighborhood sharing particle swarm optimization

Multilevel Collaborative Aerodynamic Design Optimization Based on Sobol’ Global Sensitivity Analysis

Contact Info

Product

Resources

About