The Experts below are selected from a list of 10848 Experts worldwide ranked by ideXlab platform
Yuhui Shi - One of the best experts on this subject based on the ideXlab platform.
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CEC - BSO-AL: Brain Storm Optimization Algorithm with Adaptive Learning Strategy
2020 IEEE Congress on Evolutionary Computation (CEC), 2020Co-Authors: Yang Shen, Shi Cheng, Jian Yang, Yuhui ShiAbstract:Swarm intelligence algorithms have been widely and successfully used to optimize many science and engineering problems, the collective behavior of the agents lead to the emergence of intelligence. These interactions among agents can be classified into three categories: exploring, emulating and learning. Brain Storm Optimization (BSO) is a novel swarm intelligence algorithm which is inspired by the human Brainstorming Process, and generates new ideas by emulating existing ideas. In this paper, a new BSO algorithm with an adaptive learning strategy (BSOAL) is proposed. By considering the evolutionary speed factor of each individual and the aggregation degree of the swarm, the proposed BSO-AL generates new individuals by exploring, emulating or learning adaptively. Comparative experiments were conducted on a set of benchmark functions with different dimensions. The experimental results show that the proposed BSO-AL algorithm outperforms the classic BSO algorithm and the other two state-of-the-art algorithms, which demonstrates the effectiveness of the learning strategy.
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A Novel Multi-Task Optimization Algorithm Based on the Brainstorming Process
IEEE Access, 2020Co-Authors: Chao Lyu, Yuhui Shi, Lijun SunAbstract:Evolutionary multi-task optimization (EMTO) is an emerging research topic in the field of evolutionary computation, which aims to simultaneously optimize several component tasks within a problem and output the best solution for each task. Since EMTO has widespread applications in solving real-world multi-task optimization problems, in recent years, some EMTO algorithms have been proposed. However, most of which are based on the multifactorial evolution framework which has difficulties in independently controlling the optimization of each component task and implementing parallel computing. To tackle this problem and enrich the EMTO algorithms’ family, this paper firstly designs a novel EMTO framework inspired by the Brainstorming Process of human beings when they solve multi-task problems. Under this framework, a novel EMTO algorithm, named as brain storm multi-task optimization (BSMTO), is presented, where the optimization for each component task and the knowledge transfer between different tasks are both implemented by the proposed Brainstorming operations. Furthermore, through investigating the knowledge transfer Process in the proposed algorithm, an enhanced BSMTO algorithm named as BSMTO-II is further proposed, where the knowledge transfer in each component task can be managed and controlled by our newly designed scheme. Finally, the proposed two algorithms are tested on benchmark problems. Experimental results show that BSMTO-II has a competitive performance compared with both classical and state-of-the-art algorithms. Moreover, the effectiveness of the proposed EMTO framework and the knowledge transfer control scheme is proved through experiments, and the key parameters settings and the algorithmic complexity are also discussed at last.
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Unified Swarm Intelligence Algorithms
Critical Developments and Applications of Swarm Intelligence, 2018Co-Authors: Yuhui ShiAbstract:In this chapter, the necessity of having developmental learning embedded in a swarm intelligence algorithm is confirmed by briefly considering brain evolution, brain development, Brainstorming Process, etc. Several swarm intelligence algorithms are looked at from a developmental learning perspective. A framework of a developmental swarm intelligence algorithm, which contains capacity developing stage and capability learning stage, is further given to help understand developmental swarm intelligence (DSI) algorithms, and to guide to design and/or implement any new developmental swarm intelligence algorithm and/or any developmental evolutionary algorithm. Following DSI, innovation is discussed and an innovation-inspired optimization (IO) algorithm is designed and developed. Finally, by combing the DSI and IO algorithm together, a unified swarm intelligence algorithm is proposed, which contains capacity developing stage and capability learning stage and with three search operators in its capability learning stage to mimic the three levels of innovations.
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CEC - A comprehensive survey of brain storm optimization algorithms
2017 IEEE Congress on Evolutionary Computation (CEC), 2017Co-Authors: Shi Cheng, Yifei Sun, Junfeng Chen, Quande Qin, Xianghua Chu, Xiujuan Lei, Yuhui ShiAbstract: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.
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CEC - An improved Brain Storm Optimization algorithm based on graph theory
2017 IEEE Congress on Evolutionary Computation (CEC), 2017Co-Authors: Gai-ge Wang, Yuhui Shi, Shi Cheng, Guo-sheng Hao, Zhihua CuiAbstract:Recently, inspired by the human Brainstorming Process, a new kind of metaheuristic algorithm, called brain storm optimization (BSO) algorithm was proposed for global optimization. Experimental results have shown its excellent performance when solving optimization problems. In order to further improve the search ability of the BSO, this paper proposes an improved BSO (IBSO) algorithm by introducing graph theory into it. In IBSO, new individuals will be generated to replace some old individuals when the BSO algorithm is in a poor status. Whether a BSO algorithm is in a poor status is determined by the length of Hamiltonian cycle, which can be obtained by transferring all the individuals into an undirected weight graph. A Hamiltonian cycle and its length will be computed according to a modified cycle algorithm. The proposed IBSO algorithm is tested on twelve benchmarks, and the experimental results illustrate its effectiveness.
Shi Cheng - One of the best experts on this subject based on the ideXlab platform.
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CEC - BSO-AL: Brain Storm Optimization Algorithm with Adaptive Learning Strategy
2020 IEEE Congress on Evolutionary Computation (CEC), 2020Co-Authors: Yang Shen, Shi Cheng, Jian Yang, Yuhui ShiAbstract:Swarm intelligence algorithms have been widely and successfully used to optimize many science and engineering problems, the collective behavior of the agents lead to the emergence of intelligence. These interactions among agents can be classified into three categories: exploring, emulating and learning. Brain Storm Optimization (BSO) is a novel swarm intelligence algorithm which is inspired by the human Brainstorming Process, and generates new ideas by emulating existing ideas. In this paper, a new BSO algorithm with an adaptive learning strategy (BSOAL) is proposed. By considering the evolutionary speed factor of each individual and the aggregation degree of the swarm, the proposed BSO-AL generates new individuals by exploring, emulating or learning adaptively. Comparative experiments were conducted on a set of benchmark functions with different dimensions. The experimental results show that the proposed BSO-AL algorithm outperforms the classic BSO algorithm and the other two state-of-the-art algorithms, which demonstrates the effectiveness of the learning strategy.
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Simplex Search-Based Brain Storm Optimization
IEEE Access, 2018Co-Authors: Wei Chen, Shi Cheng, Yingying Cao, Yifei Sun, Qunfeng LiuAbstract:Through modeling human’s Brainstorming Process, the brain storm optimization (BSO) algorithm has become a promising population-based evolutionary algorithm. However, BSO is pointed out that it possesses a degenerated L-curve phenomenon, i.e., it often gets near optimum quickly but needs much more cost to improve the accuracy. To overcome this question in this paper, an excellent direct search-based local solver, the Nelder–Mead Simplex method is adopted in BSO. Through combining BSO’s exploration ability and NMS’s exploitation ability together, a simplex search-based BSO (Simplex-BSO) is developed via a better balance between global exploration and local exploitation. Simplex-BSO is shown to be able to eliminate the degenerated L-curve phenomenon on unimodal functions, and alleviate significantly this phenomenon on multimodal functions. Large number of experimental results shows that Simplex-BSO is a promising algorithm for global optimization problems.
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CEC - A comprehensive survey of brain storm optimization algorithms
2017 IEEE Congress on Evolutionary Computation (CEC), 2017Co-Authors: Shi Cheng, Yifei Sun, Junfeng Chen, Quande Qin, Xianghua Chu, Xiujuan Lei, Yuhui ShiAbstract: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.
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CEC - An improved Brain Storm Optimization algorithm based on graph theory
2017 IEEE Congress on Evolutionary Computation (CEC), 2017Co-Authors: Gai-ge Wang, Yuhui Shi, Shi Cheng, Guo-sheng Hao, Zhihua CuiAbstract:Recently, inspired by the human Brainstorming Process, a new kind of metaheuristic algorithm, called brain storm optimization (BSO) algorithm was proposed for global optimization. Experimental results have shown its excellent performance when solving optimization problems. In order to further improve the search ability of the BSO, this paper proposes an improved BSO (IBSO) algorithm by introducing graph theory into it. In IBSO, new individuals will be generated to replace some old individuals when the BSO algorithm is in a poor status. Whether a BSO algorithm is in a poor status is determined by the length of Hamiltonian cycle, which can be obtained by transferring all the individuals into an undirected weight graph. A Hamiltonian cycle and its length will be computed according to a modified cycle algorithm. The proposed IBSO algorithm is tested on twelve benchmarks, and the experimental results illustrate its effectiveness.
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Brain storm optimization algorithm: a review
Artificial Intelligence Review, 2016Co-Authors: Shi Cheng, Junfeng Chen, Quande Qin, Yuhui ShiAbstract:For swarm intelligence algorithms, each individual in the swarm represents a solution in the search space, and it also can be seen as a data sample from the search space. Based on the analyses of these data, more effective algorithms and search strategies could be proposed. Brain storm optimization (BSO) 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. In this paper, the history development, and the state-of-the-art of the BSO algorithm are reviewed. In addition, the convergent operation and divergent operation in the BSO algorithm are also discussed from the data analysis perspective. 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. Swarm intelligence and data mining techniques can be combined to produce benefits above and beyond what either method could achieve alone.
Paul B Paulus - One of the best experts on this subject based on the ideXlab platform.
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alternating individual and group idea generation finding the elusive synergy
Journal of Experimental Social Psychology, 2017Co-Authors: Runa M Korde, Paul B PaulusAbstract:Abstract Three experiments were designed to test the efficacy of ideation procedures that involved alternation of individual and group idea generation sessions (hybrid Brainstorming) as compared to traditional individual and group ideation. The hybrid condition led to the best performance in terms of number of ideas generated. This effect was strongest in comparison to the group condition. A meta-analytic comparison involving all three experiments indicated that the hybrid condition outperformed both the alone and the group conditions. Since after each group idea exchange session there was an enhancement in the number of ideas generated in the alone session, the pattern of performance in the hybrid condition supported the cognitive perspective of group creativity (Nijstad & Stroebe, 2006; Paulus & Brown, 2007). Social cues in the form of the co-presence of other participants in the alone condition, the addition of practice sessions to all conditions, and an additional phase did not change the pattern of results. The results of the experiments support the original suggestion by Osborn (1953) that the most effective Brainstorming Process is one that involves a variation in individual and group ideation.
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toward more creative and innovative group idea generation a cognitive social motivational perspective of Brainstorming
Social and Personality Psychology Compass, 2007Co-Authors: Paul B Paulus, Vincent R BrownAbstract:In many meetings and work sessions, group members exchange ideas in order to come up with novel, creative solutions for problems and to generate ideas for future innovations. This type of group idea generation or Brainstorming Process has been studied in detail, and we have discovered much about the cognitive and social Processes that underlie group idea generation. It appears that the Brainstorming performance of groups is often hindered by various social and cognitive influences, but under the appropriate conditions, group idea exchange can be quite effective. In this article, we summarize the present state of knowledge, point out some significant gaps in our knowledge, and suggest a cognitive-social-motivational perspective to integrate the major findings and to guide future research in the area of group creativity and group idea generation.
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Toward More Creative and Innovative Group Idea Generation: A Cognitive‐Social‐Motivational Perspective of Brainstorming
Social and Personality Psychology Compass, 2007Co-Authors: Paul B Paulus, Vincent R BrownAbstract:In many meetings and work sessions, group members exchange ideas in order to come up with novel, creative solutions for problems and to generate ideas for future innovations. This type of group idea generation or Brainstorming Process has been studied in detail, and we have discovered much about the cognitive and social Processes that underlie group idea generation. It appears that the Brainstorming performance of groups is often hindered by various social and cognitive influences, but under the appropriate conditions, group idea exchange can be quite effective. In this article, we summarize the present state of knowledge, point out some significant gaps in our knowledge, and suggest a cognitive-social-motivational perspective to integrate the major findings and to guide future research in the area of group creativity and group idea generation.
Qunfeng Liu - One of the best experts on this subject based on the ideXlab platform.
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Simplex Search-Based Brain Storm Optimization
IEEE Access, 2018Co-Authors: Wei Chen, Shi Cheng, Yingying Cao, Yifei Sun, Qunfeng LiuAbstract:Through modeling human’s Brainstorming Process, the brain storm optimization (BSO) algorithm has become a promising population-based evolutionary algorithm. However, BSO is pointed out that it possesses a degenerated L-curve phenomenon, i.e., it often gets near optimum quickly but needs much more cost to improve the accuracy. To overcome this question in this paper, an excellent direct search-based local solver, the Nelder–Mead Simplex method is adopted in BSO. Through combining BSO’s exploration ability and NMS’s exploitation ability together, a simplex search-based BSO (Simplex-BSO) is developed via a better balance between global exploration and local exploitation. Simplex-BSO is shown to be able to eliminate the degenerated L-curve phenomenon on unimodal functions, and alleviate significantly this phenomenon on multimodal functions. Large number of experimental results shows that Simplex-BSO is a promising algorithm for global optimization problems.
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Improving Brain Storm Optimization Algorithm via Simplex Search.
2017Co-Authors: Wei Chen, Yingying Cao, Yifei Sun, Qunfeng LiuAbstract:Through modeling human's Brainstorming Process, the brain storm optimization (BSO) algorithm has become a promising population based evolution algorithm. However, BSO is often good at global exploration but not good enough at local exploitation, just like most global optimization algorithms. In this paper, the Nelder-Mead's Simplex (NMS) method is adopted in a simple version of BSO. Our goal is to combine BSO's exploration ability and NMS's exploitation ability together, and develop an enhanced BSO via a better balance between global exploration and local exploitation. Large number of experimental results are reported, and the proposed algorithm is shown to perform better than both BSO and NMS.
Yifei Sun - One of the best experts on this subject based on the ideXlab platform.
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Simplex Search-Based Brain Storm Optimization
IEEE Access, 2018Co-Authors: Wei Chen, Shi Cheng, Yingying Cao, Yifei Sun, Qunfeng LiuAbstract:Through modeling human’s Brainstorming Process, the brain storm optimization (BSO) algorithm has become a promising population-based evolutionary algorithm. However, BSO is pointed out that it possesses a degenerated L-curve phenomenon, i.e., it often gets near optimum quickly but needs much more cost to improve the accuracy. To overcome this question in this paper, an excellent direct search-based local solver, the Nelder–Mead Simplex method is adopted in BSO. Through combining BSO’s exploration ability and NMS’s exploitation ability together, a simplex search-based BSO (Simplex-BSO) is developed via a better balance between global exploration and local exploitation. Simplex-BSO is shown to be able to eliminate the degenerated L-curve phenomenon on unimodal functions, and alleviate significantly this phenomenon on multimodal functions. Large number of experimental results shows that Simplex-BSO is a promising algorithm for global optimization problems.
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Improving Brain Storm Optimization Algorithm via Simplex Search.
2017Co-Authors: Wei Chen, Yingying Cao, Yifei Sun, Qunfeng LiuAbstract:Through modeling human's Brainstorming Process, the brain storm optimization (BSO) algorithm has become a promising population based evolution algorithm. However, BSO is often good at global exploration but not good enough at local exploitation, just like most global optimization algorithms. In this paper, the Nelder-Mead's Simplex (NMS) method is adopted in a simple version of BSO. Our goal is to combine BSO's exploration ability and NMS's exploitation ability together, and develop an enhanced BSO via a better balance between global exploration and local exploitation. Large number of experimental results are reported, and the proposed algorithm is shown to perform better than both BSO and NMS.
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CEC - A comprehensive survey of brain storm optimization algorithms
2017 IEEE Congress on Evolutionary Computation (CEC), 2017Co-Authors: Shi Cheng, Yifei Sun, Junfeng Chen, Quande Qin, Xianghua Chu, Xiujuan Lei, Yuhui ShiAbstract: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.