The development, implementation, variant, and future directions of a new swarm intelligence algorithm, brain storm optimization (BSO) algorithm, are comprehensively surveyed. Brain storm optimization algorithm is a new and promising swarm intelligence algorithm, which simulates the human brainstorming process. Through the convergent operation and divergent operation, individuals in BSO are grouped and diverged in the search space/objective space. To the best of our knowledge, there are 75 papers, 8 theses, and 5 patents in total on the development and application of the BSO algorithm. Every individual in the BSO algorithm is not only a solution to the problem to be optimized, but also a data point to reveal the landscape of the problem. Based on the developments of brain storm optimization algorithms, different kinds of optimization problems and real-world applications could be solved.

A comprehensive survey of brain storm optimization algorithms

Demand-side management and incentive-based optimization have the potential to improve energy efficiency of modern smart homes and smart communities. Existing approaches only refer to consumers’ comfort level to thermal-related electric appliances. Other controllable appliances may not be included in the incentive designs, and the total community power consumption is somehow neglected. Thus, the involvement of residents’ participation is limited. To address this issue, we propose a new incentive-based residential energy optimization system to manage community demand reduction requests efficiently and, meanwhile, reward consumers with multi-level financial incentives and guaranteed comfort. A new design of comfort indicator is used, which considers both thermal and major controllable electric appliances based on the consumers’ comfort level. We integrate a genetic algorithm to solve this optimization problem, i.e., to minimize the reward costs for the utility (according to maximize the consumers’ comfort level). As an alternative approach, the mixed integer programming technique is also employed if the objective function includes a certain piecewise linear decision variable. Simulation studies on both 10-house and 100-house cases show that the proposed approach is outperformed two existing approaches in terms of reward incentives, comfort levels, and the number of active appliances.

A New Incentive-Based Optimization Scheme for Residential Community With Financial Trade-Offs

The original brain storm optimization (BSO) fails to consider some potential information interactions in its individual update pattern, causing the premature convergence for complex problems. To address this problem, we propose a BSO algorithm with multi-information interactions (MIIBSO). First, a multi-information interaction (MII) strategy is developed, thoroughly considering various information interactions among individuals. Specially, this strategy contains three new MII patterns. The first two patterns aim to reinforce information interaction capability between individuals. The third pattern provides interactions between the corresponding dimensions of different individuals. The collaboration of the above three patterns is established by an individual stagnation feedback mechanism, contributing to preserve the diversity of the population and enhance the global search capability for MIIBSO. Second, a random grouping (RG) strategy is introduced to replace both the K-means algorithm and cluster center disruption of the original BSO algorithm, further enhancing the information interaction capability and reducing the computational cost of MIIBSO. Finally, a dynamic difference step-size (DDS), which can offer individual feedback information and improve search range, is designed to achieve an effective balance between global and local search capability for MIIBSO. By combining the MII strategy, RG, and DDS, MIIBSO achieves the effective improvement in the global search ability, convergence speed, and computational cost. MIIBSO is compared with 11 BSO algorithms and five other algorithms on the CEC2013 test suit. The results confirm that MIIBSO obtains the best global search capability and convergence speed amongst the 17 algorithms.

A Brain Storm Optimization With Multi-Information Interactions for Global Optimization Problems

The distributed hybrid flowshop scheduling (DHFS) problem is a common scheduling problem that has been researched in both academic and industrial fields during recent years. The uncertainty levels in realistic applications are generally too high to be represented by a deterministic value or a triangular fuzzy number (TFN) value. Considering the DHFS problem with type-2 fuzzy processing time and setup time constraints, an improved version of brain storm optimization was developed, where the objective is to minimize the maximum type-2 fuzzy completion time among all factories. The main contributions of this study are as follows: (1) each solution is represented by a two vectors, i.e., a scheduling vector and a factory assignment vector; (2) two realistic constraints, i.e., the type-2 fuzzy processing time in an uncertain environment and the setup time, make the problem more realistic; (3) a novel constructive heuristic based on the Nawaz-Enscore-Ham (NEH) method, called distributed NEH, is proposed; (4) several local search heuristics considering the problem features and the objective are developed to enhance the local search abilities; and (5) a simulated-annealing-based acceptance criterion is embedded to enhance the exploration abilities. The experimental results demonstrate that the proposed algorithm is more efficient and effective for solving the considered type-2 fuzzy DHFS problems in comparison with other recently published efficient algorithms.

Solving Type-2 Fuzzy Distributed Hybrid Flowshop Scheduling Using an Improved Brain Storm Optimization Algorithm

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 article, 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 article 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.

/pdf/an-agglomerative-greedy-brain-storm-optimization-algorithm-4ugjlupar4.pdf

An Agglomerative Greedy Brain Storm Optimization Algorithm for Solving the TSP

This paper presents a novel approach based on the combination of the Modified Brain Storm Optimization algorithm (MBSO) with a simplified version of the Constraint Consensus method as special operator to solve constrained numerical optimization problems. Regarding the special operator, which aims to reach the feasible region of the search space, the consensus vector becomes the feasibility vector computed by the hardest constraint in turn for a current infeasible solution; then the operations to mix the other feasibility vectors are avoided. This new combined algorithm, named as MBSO-R+V, solves a suit of eighteen test problems in ten and thirty dimensions. From a set of experiments related to the location and frequency of application of the constraint consensus method within MBSO, a suitable design of the combined approach is presented. This proposal shows encouraging final results while being compared against state-of-the-art algorithms, showing that it is viable to add special operators to improve the capabilities of swarm-intelligence algorithms when dealing with continuous constrained search spaces.

A modified brain storm optimization algorithm with a special operator to solve constrained optimization problems

This paper presents an empirical analysis of two well-known crossover operators in real-coded genetic algorithms: Blend Crossover (BLX-a) and Simulated Binary Crossover (SBX), for constrained numerical optimization problems. The aim of the study is to analyze the ability of each operator to generate feasible solutions and also suggest suitable variation operator parameter values for such purpose. A performance measure is proposed to evaluate the capacity of each operator to find feasible offspring. A set of fourteen benchmark problems is used in the experiments. The results show that in both crossover operators the exploration ability must be enhanced so as to get better results.

An empirical comparison of two crossover operators in real-coded genetic algorithms for constrained numerical optimization problems

A study on three BSO algorithm versions: Brain Storm Optimization Algorithm (BSO), Modified Brain Storm Optimization Algorithm (MBSO) and Simple Modified Brain Storm Optimization Algorithm (SMBSO), for constrained numerical optimization problems is presented in this paper. The aim of the study is to know the performance of this recent Swarm Intelligence (SI) algorithm on constrained search spaces. The feasibility rules, e-constrained method, and stochastic ranking are used as constraint-handling techniques. The performance of each version is analysed by solving 24 well-known benchmark problems. The final results suggest MBSO and the e-constrained method as a good option to deal with constrained problems.

A study of constraint-handling techniques in brain storm optimization

In this paper a special Boundary Search operator is added to the Brain Storm Optimization algorithm leading to the search process towards the feasibility boundary of the hardest current constraint. Two new boundary search operator versions are introduced inspired in the analytical geometry division line segment topic and the gradient method, respectively. The algorithm performance is analysed by solving 36 well-know constrained problems, reporting an improvement degree in the range of 22% to 27% of the total of functions in 10D and 50% to 66% of the total of functions in 30D compared with the eDEag algorithm.

Adriana Cervantes-Castillo

Papers

A modified brain storm optimization algorithm with a special operator to solve constrained optimization problems

An empirical comparison of two crossover operators in real-coded genetic algorithms for constrained numerical optimization problems

A study of constraint-handling techniques in brain storm optimization

Exploring boundary constraints using the modified brain storm optimization algorithm and a boundary search operator: MBSO-BS