作业车间动态调度建模与仿真研究

针对作业车间动态调度问题进行了分析和建模,应用多微粒群协同优化理论,提出了一种制造业作业车间动态调度优化算法,通过基于Matlab的模拟与仿真,验证了本算法的有效性和高效性.     

基于细胞因子网络协同进化的Web服务合成方法

借鉴生物神经内分泌免疫系统的协同进化机制,基于细胞因子网络平台提出了Web服务的合成方法.在细胞因子网络调控下的生物实体代理Web服务,构成为一个带有条件的米兰机单元,Web服务的合成可以转换为米兰机进化过程.生物实体通过消息匹配和条件约束形成细胞因子网络,Web服务的合成通过生物实体细胞因子网络支持.在服务协同进化过程中可以动态调整其合成的服务,完成服务的动态自组织合成和管理等工作.仿真结果表明该方法在环境动态变化时具有适应性.     

融合微粒群的多种群协同进化免疫算法

提出一种融合微粒群的多种群协同免疫优势克隆选择算法(PMCICA).该算法将生态学中的协同进化思想引入人工免疫算法中,各子种群内部通过免疫优势克隆选择操作加快了种群收敛速度;所有子种群共享经过改进微粒群优化的高层优良库,实现了整个种群信息共享与协同进化.针对旅行商问题(TSP)的多个实验结果表明,该算法在收敛速度与最优解等方面均取得了较好的效果.     

基于改进异质协同演化的测试用例生成研究

路径搜索是测试用例自动生成的重要环节。针对遗传算法在测试用例生成中的“早熟”缺陷,提出一种改进的异质协同演化算法,将种群划分成两个子种群,分别采用遗传子群和差分子群进行演化,在演化的过程中两个子种群相互协作,通过改进迁移间隔代数和迁移率这两个参数,增加扰动,更加均衡遗传算法的全局探索与差异演化算法的局部搜索。实验结果表明,该算法比遗传算法和传统异质协同演化算法在生成测试用例的收敛性能方面更具优势,因此该方法更适合测试用例自动生成的应用中。     

Decomposition-based design optimization method using genetic co-evolution

The paper examines new strategies for the implementation of genetic algorithms in a decomposition-based approach for design. In this approach, the design problem is decomposed into smaller-sized sub-problems, the solutions for which are obtained through co-evolution. The emphasis resides in evaluating methods for exchanging design information relevant to coordinating solutions of temporarily decoupled sub-problems. Methods based on modification of genetic makeup through experiential inheritance (exposure to another species), and through inter-species migration are investigated in this work. Different forms of design problem coupling are investigated, ranging from coupling through constraints only, to coupled objective and constraint functions. The proposed strategies are validated through implementation in representative algebraic and structural design problems.     

Co-evolution between streaming and live music leads a way to the sustainable growth of music industry – Lessons from the US experiences

While digitization of music, particularly streaming services, has gained increasing popularity, it has also led to a steady decline in the revenues of recorded music industry. This is causing strong concern regarding a potential collapse of the music industry comparable to other print media industries such as newspaper and book publishing.However, recent changes in the music industry initiated by a resurgence of the live music industry are giving rise to some expectations for the survival and growth of the music industry. The parallel paths of increasing popularity of streaming services and a resurgence of live music suggest that these two dynamics are working together in a co-evolutionary way toward the sustainability of the music industry.This paper attempts to elucidate the co-evolutionary dynamism between the increasing popularity of streaming music and the resurgence of live music.An empirical analysis of monthly trends over the period of the last three decades in the US music industry by its sectors revealed that (i) the co-evolution between streaming and live music industries has functioned well over the last few years, (ii) the live music industry has incorporated a self-propagating function by assimilating innovations previously initiated by digital music, (iii) given the above co-evolution, the recent resurging trend in the music industry can be sustained, (iv) the advancement of digital innovations such as artificial intelligence, machine learning, fintech, virtual reality, big data, and social media by enabling such coevolution have transformed the live music industry into a “live-concert-streaming music industry” (LCSMI) that further enabling the participative creativity of its stakeholders. For these collaborative and cultural industries to function in harmony, trust between its participating stakeholders is very crucial.This analysis suggests the significance of a trust-based ICT-driven disruptive business model (IDBM) with a consolidated challenge for social demand (CCSD) for the development of cultural industries.     

Searching efficient plans for emergency rescue through simulation: the case of a metro fire

In this paper we present the way we modelled and simulated a metro system in the case of a tunnel fire, and discuss the ways this simulation may support the search for efficient rescue plans. The metro system was modelled as a complex adaptive system, comprising four interacting and co-evolving subsystems: (i) the fire and the released smoke, (ii) the group of passengers, (iii) the technological system, and (iv) the metro personnel. Based on this model, an agent-based simulation was developed. This simulation provides an appropriate dynamic representation of the designers problem space, enabling him (i) to apprehend the critical dependencies and invariants of the system under consideration, (ii) to identify the features that should characterise the designed emergency rescue plans, and (iii) to assess their efficiency. To demonstrate the usefulness of the adopted approach for the design of an efficient emergency rescue plan, the results of two experiments exploring alternative sequences of the metro personnels actions under different circumstances are presented.     

Modelling opinion formation driven communities in social networks

In a previous paper we proposed a model to study the dynamics of opinion formation in human societies by a co-evolution process involving two distinct time scales of fast transaction and slower network evolution dynamics. In the transaction dynamics we take into account short range interactions as discussions between individuals and long range interactions to describe the attitude to the overall mood of society. The latter is handled by a uniformly distributed parameter α, assigned randomly to each individual, as quenched personal bias. The network evolution dynamics is realised by rewiring the societal network due to state variable changes as a result of transaction dynamics. The main consequence of this complex dynamics is that communities emerge in the social network for a range of values in the ratio between time scales. In this paper we focus our attention on the attitude parameter α and its influence on the conformation of opinion and the size of the resulting communities. We present numerical studies and extract interesting features of the model that can be interpreted in terms of social behaviour.