Grid-based dynamic robust multi-objective brain storm optimization algorithm

Guo, Yinan; Yang, Huan; Chen, Meirong; Gong, Dunwei; Cheng, Shi

doi:10.1007/s00500-019-04365-w

Cited by 28 publications

(11 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithm introduces the strategy of archive set with non-dominated solutions in each iteration to get a group of solution close enough to Pareto front and uniform solution. A grid-based method and a hybrid mutation strategy integrating above traditional Gaussian-, Cauchy-and Chaotic-based mutation replace k-means clustering and Gaussian mutation in [50] to enhance the diversity and avoiding the premature convergence. A random probabilistic decision-making of river formation dynamics scheme to select optimal cluster centroids during population generation step, and an adaptive mutation operator were taken in [51] to improve the performance of IMBSO.…”

Section: B Multi-objective Brain Storm Optimizationmentioning

confidence: 99%

Many-Objective Brain Storm Optimization Algorithm

Wang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

In recent years, many evolutionary algorithms and population-based algorithms have been developed for solving many-objective optimization problems. Inspired by the human brainstorming conference, Brain Storming Optimization (BSO) algorithm was guided by the cluster centers and other individuals with probability, which can balance convergence and diversity greatly. In this paper, the authors propose a novel brain storm optimization algorithm for many-objective optimization problem. The algorithm adopts the decision variable clustering method to divides the variables into convergence-related variables and diversityrelated variables. The decomposition strategy is designed to increases selection pressure for the convergencerelated variables, while the reference point's strategy is adopted for the diversity-related variables to update the population and increase the diversity. Experimental results show that the proposed many-objective brain storm optimization algorithm is a very promising algorithm for solving many-objective optimization problems.

show abstract

Section: B Multi-objective Brain Storm Optimizationmentioning

confidence: 99%

Many-Objective Brain Storm Optimization Algorithm

Wang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…A large number of scholars have paid increasing attention to BSO and have conducted in-depth research on it. According to the algorithm mechanism and application background, the research on the BSO algorithm can be divided into the following categories: (1) improving the clustering method of BSO [28], [29], [30], [31], [32], [33], [34], [35], [36]; (2) improving the new individual generation strategy [34], [37], [38], [39], [40], [41], [42], [43], [44]; (3) applying the research on BSO [9], [12], [15], [18], [38], [45], [46], [47]. The improvement and research of these algorithms from different directions not only improve the optimization performance of BSO but also promote the healthy development of BSO theory and applications.…”

Section: Introductionmentioning

confidence: 99%

“…In 2019, Yu et al [44] proposed distance-based diversity and fitness-based diversity to improve the diversity of the BSO population and to adapt the algorithm parameters. In 2020, Guo et al [43] proposed a grid-based multiple objective BSO with a hybrid mutation operation, which drew on the idea of Cauchy and chaotic mutation operators and generated new individuals with wide diversity. In 2020, Sun et al [37] proposed a novel BSO (RMBSO) in view of the fact that the original BSO tends to stagnate in the exploitation phase.…”

Section: Introductionmentioning

confidence: 99%

“…Although scholars have made some progress in the theory and application of BSO, there are still some key issues. The enhancement of the BSO algorithm proposed in [29], [30], [34], and [43] involve modifying only the clustering method and the strategy for generating the population. However, most BSO algorithms still tend to fall into local optima when solving complex optimization problems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Agglomerative Greedy Brain Storm Optimization Algorithm for Solving the TSP

2020

IEEE Access

View full text Add to dashboard Cite

The brain storm optimization algorithm(BSO) is a population based metaheuristic algorithm inspried by the human conferring process that was proposed in 2010. Since its first implementation, BSO has been widely used in various fields. In this paper, we propose an agglomerative greedy brain storm optimization algorithm (AG-BSO) to solve classical traveling salesman problem(TSP). Due to the low accuracy and slow convergence speed of current heuristic algorithms when solving TSP, this paper consider four improvement strategies for basic BSO. First, a greedy algorithm is introduced to ensure the diversity of the population. Second, hierarchical clustering is used in place of the k-means clustering algorithm in standard BSO to eliminate the noise sensitivity of the original BSO algorithm when solving TSP. Exchange rules for the individuals in the population individuals were introduced to improve the efficiency of the algorithm. Finally, a heuristic crossover operator is used to update the individuals. In addition, the AG-BSO algorithm is compared with the genetic algorithm (GA), particle swarm optimization (PSO), the simulated annealing(SA) and the ant colony optimization (ACO) on standard TSP data sets for performance testing. We also compare it with a recently improved version of the BSO algorithm. The simulations show the encouraging results that AG-BSO greatly improved the solution accuracy, optimization speed and robustness.INDEX TERMS brain storm optimization algorithm, traveling salesman problem, hierarchical clustering, optimization algorithm, combinatorial optimization.

show abstract

“…The use of the latter distribution has a higher probability of making longer jumps than the former one, due to its long flat tails. Differential Evolution, Chaotic and hybrid mutation strategies have been considered to optimize the performance of BSO [ 7 , 14 , 32 , 34 ] by avoiding premature convergence. In [ 8 , 21 ], the predator-prey method has been proposed for better utilization of the global information of the swarm and diversification of the population.…”

Section: Introductionmentioning

confidence: 99%

Determinative Brain Storm Optimization

Sovatzidi

Iakovidis

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Brain Storm Optimization (BSO) is a swarm intelligence optimization algorithm, based on the human brainstorming process. The ideas of a brainstorming process comprise the solutions of the algorithm, which iteratively applies solution grouping, generation and selection operators. Several modifications of BSO have been proposed to enhance its performance. In this paper, we propose a novel modification enabling faster convergence of BSO to optimal solutions, without requiring setting an upper bound of algorithm iterations. It considers a brainstorming scenario where participating groups with similar ideas recognize that their ideas are similar, and together, collaborate for the determination of a better solution. The proposed modification, called Determinative BSO (DBSO), implements this scenario by applying a cluster merging strategy for merging groups of similar solutions, while following elitist selection. Experimental results using eleven benchmark functions show that the proposed modified BSO performs better than both the original and a state-of-the-art algorithm.

show abstract

Grid-based dynamic robust multi-objective brain storm optimization algorithm

Cited by 28 publications

References 20 publications

Many-Objective Brain Storm Optimization Algorithm

Many-Objective Brain Storm Optimization Algorithm

An Agglomerative Greedy Brain Storm Optimization Algorithm for Solving the TSP

Determinative Brain Storm Optimization

Contact Info

Product

Resources

About