Narrow Results

Improved ant colony optimization (ACO) algorithms for continuous-domain optimization have been widely applied in recent years, but these improved methods have a weak perception of environmental information changes and only rely on the residues of the pheromones in the path to guide colony evolution. In this paper, we propose an ant colony algorithm based on the reinforcement learning model (RLACO). RLACO can acquire more environmental information by calculating the diversity of the ant colony, and, uses the diversity and other basic information of the ant colony to establish a reinforcement learning model. At different stages of evolution, the algorithm chooses an optimal strategy that can maximize the reward to improve the global search ability and convergence speed of the colony. The experimental results on CEC 2017 test functions show that the proposed algorithm is superior to other algorithms for continuous-domain optimization in convergence speed, accuracy and global search ability.

In this paper, formation of diverse groups of individuals based on attributes and two diversity measures are discussed. The formation of clusters in a diversity space is described. An algorithm is given for diverse clustering based on separation in the space and not the nearness using application based diversity thresholds and number of clusters.

There are growing interests for studying collective behavior including the dynamics of markets, the emergence of social norms and conventions and collective phenomena in daily life such as traffic congestion. In our previous work [Iwanaga and Namatame, Collective behavior and diverse social network, International Journal of Advancements in Computing Technology4(22) (2012) 321–320], we showed that collective behavior in cooperative relationships is affected in the structure of the social network, the initial collective behavior and diversity of payoff parameter. In this paper, we focus on scale-free network and investigate the effect of number of interactions on collective behavior. And we found that choices of hub agents determine collective behavior.

Diversity in teams has become an important societal and economic issue which is studied in various scientific domains. In organizational sciences, particularly empirical research methods prevail. This paper proposes to explore agent-based computational economics as a research approach for workforce diversity more intensely due to its inherent properties like capturing heterogeneous interacting agents. For highlighting this, this paper presents an agent-based computational model based on the framework of NK fitness landscapes. In the simulations, artificial organizations search for superior levels of organizational performance with search being delegated to several and potentially diverse decision-making agents. The experiments control for the level of task complexity and reflects four different attributes of workplace diversity among agents: cognitive capabilities to (i) generate and (ii) evaluate new solutions, (iii) effort efficiency and (iv) commitment to the overall organizational objective. The results suggest that the effects of workforce diversity differ across task complexity and attributes of diversity. Diversity of commitment has the strongest impact which results from interactions among local maximizers and agents seeking to globally maximize with only local means. Moreover, the results point to nonlinear effects of multi-attributive diversity on organizational performance.

Two fields of research have found tremendous applicability in the analysis of biological data-statistics and information theory. Statistics is extensively used for the measurement of central tendency, dispersion, comparison and covariation. Measures of information are used to study diversity and equitability. These two fields have been used independent of each other for data analysis. In this communication, we develop the link between the two and prove that statistical measures can be used as information measures. Our study will be a new interdisciplinary field of research and it will be possible to describe information content of a system from its statistics.