Global-best Brain Storm Optimization Research Articles

Global-best brain storm optimization algorithm based on chaotic difference step and opposition-based learning

Recently, the following global-best strategy and discussion mechanism have been prevailing to solve the slow convergence and the low optimization accuracy in the brain storm optimization (BSO) algorithm. However, the traditional BSO algorithm also suffers from the problem that it is easy to fall into local optimum. Therefore, this work innovatively designed the chaotic difference step strategy. This strategy introduced four commonly used chaotic maps and difference step to expand the population search space to improve the situation. Moreover, opposition-based learning thought was innovatively adopted into the BSO algorithm. The thought aims to generate the opposition-based population, increase the search density, and make the algorithm out of the local optimum as soon as possible. In summary, this work proposed a global-best brain storm optimization algorithm based on the chaotic difference step and opposition-based learning (COGBSO). According to the CEC2013 benchmark test suit, 15 typical benchmark functions were selected, and multiple sets of simulation experiments were conducted on MATLAB. The COGBSO algorithm was also compared to recent competitive algorithms based on the complete CEC2018 benchmark test suit. The results demonstrate that the COGBSO outperforms BSO and other improved algorithms in solving complex optimization problems.

Segmentation of thermographies from electronic systems by using the global-best brain storm optimization algorithm

Segmentation of thermographies from electronic systems by using the global-best brain storm optimization algorithm

Alternate search pattern-based brain storm optimization

Brain storm optimization (BSO) groups population into several clusters and generates new individuals by using the information of these clusters. However, this mechanism limits the ability of exploration because it prevents new individuals from searching regions far away from current clusters. In this paper, we innovatively propose a grid-based search operator (GBS) to improve the exploration by dividing the given search space into smaller ones. Then, we modify the cluster, replacement, and mutation strategy of the original BSO for requiring a better exploitation. Besides, an alternate search pattern (ASP) strategy is designed for controlling the transformation between GBS and BSO to balance exploration and exploitation. Finally, two variants of BSO have been proposed based on the original BSO and a global-best BSO, and termed as ABSO and AGBSO, respectively. The proposed ABSO and AGBSO are tested on a number of widely used benchmark optimization problems. The comparative analysis shows that ASP strategy can significantly improve the performance of BSO in terms of solution quality and population diversity. Additionally, AGBSO can be considered as a state-of-the-art BSO among all its variants. The source code of all proposed methods can be found at https://toyamaailab.github.io/sourcedata.html.

Distribution State Estimation using Multiple Stages considering Asynchronous Measurement Data by Dependable Parallel Multi-population Global-best Brain Storm Optimization with Differential Evolution Strategies

Distribution State Estimation using Multiple Stages considering Asynchronous Measurement Data by Dependable Parallel Multi-population Global-best Brain Storm Optimization with Differential Evolution Strategies

Total Optimization of Energy Networks in a Smart City by Multi-Population Global-Best Modified Brain Storm Optimization with Migration

This paper proposes total optimization of energy networks in a smart city by multi-population global-best modified brain storm optimization (MP-GMBSO). Efficient utilization of energy is necessary for reduction of CO2 emission, and smart city demonstration projects have been conducted around the world in order to reduce total energies and the amount of CO2 emission. The problem can be formulated as a mixed integer nonlinear programming (MINLP) problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolution (DE), Differential Evolutionary Particle Swarm Optimization (DEEPSO), Brain Storm Optimization (BSO), Modified BSO (MBSO), Global-best BSO (BSO), and Global-best Modified Brain Storm Optimization (GMBSO) have been applied to the problem. However, there is still room for improving solution quality. Multi-population based evolutionary computation methods have been verified to improve solution quality and the approach has a possibility for improving solution quality. The proposed MS-GMBSO utilizes only migration for multi-population models instead of abest, which is the best individual among all of sub-populations so far, and both migration and abest. Various multi-population models, migration topologies, migration policies, and the number of sub-populations are also investigated. It is verified that the proposed MP-GMBSO based method with ring topology, the W-B policy, and 320 individuals is the most effective among all of multi-population parameters.

Optimal Operational Planning of Energy Plants Considering Uncertainty of Renewable Energy Outputs by Global-best Brain Storm Optimization

Abstract This paper proposes optimal operational planning of energy plants considering (OPEP) renewable energy (RE)’s uncertainty. In recent years, global warming is exacerbated by the increase in the carbon dioxide’s emission which belongs to greenhouse gases. Utilization of renewable energies is necessary in order to reduce its emissions. RE can also be utilized as emergency power supplies for Business Continuity Planning (BCP). Since the deterministic operational planning problem of energy plants is one of mixed-integer nonlinear optimization programming (MINLP) problems, conventional mathematical programming cannot solve it easily. Hence, it has been solved by various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolutionary PSO (DEEPSO), modified brain storm optimization (MBSO), and global-best brain storm optimization (GBSO). In addition, considering uncertainty of RE outputs, Monte Carlo simulation should be utilized. The proposed Monte Carlo simulation based method using GBSO is compared with the deterministic GBSO based method. It was verified the proposed Monte Carlo simulation based method using GBSO can consider uncertainty of renewable energies appropriately.

Global-best brain storm optimization algorithm

Abstract Brain storm optimization (BSO) is a population-based metaheuristic algorithm that was recently developed to mimic the brainstorming process in humans. It has been successfully applied to many real-world engineering applications involving non-linear continuous optimization. In this work, we propose improving the performance of BSO by introducing a global-best version combined with per-variable updates and fitness-based grouping. In addition, the proposed algorithm incorporates a re-initialization scheme that is triggered by the current state of the population. The introduced Global-best BSO (GBSO) is compared against other BSO variants on a wide range of benchmark functions. Comparisons are based on final solutions and convergence characteristics. In addition, GBSO is compared against global-best versions of other meta-heuristics on recent benchmark libraries. Results prove that the proposed GBSO outperform previous BSO variants on a wide range of classical functions and different problem sizes. Moreover, GBSO outperforms other global-best meta-heuristic algorithms on the well-known CEC05 and CEC14 benchmarks.