Ant Colony Intelligence Research Articles

The Clustering Effect Analysis of College Marxist Theory Network Propagation Based on Ant Colony Intelligent Algorithm

Abstract Marxist ideology is the mainstream ideology of China, and its effective transmission is to consolidate the ideological foundation and soul of the socialist system with Chinese characteristics. The effective transmission of Marxist thought in the new media environment will open up a new road for media development. In the development process of new media, it can work with mainstream ideology as the core. Based on the ant colony intelligence method, this paper discusses the effect of college Marxism theory on the Internet. The model uses an optimization method based on local feasibility and descending order to modify the pheromone of this method. The results show that the improved ant algorithm can efficiently distribute the load in Marx’s theoretical network.

Comparative Analytical Study Considering The Analogy of Learning Creativity Quantification versus Ant Colony Intelligence

This piece of research introduces an investigational systematic study of an interdisciplinary challenging phenomenon observed in natural world. Interestingly, this study belongs to the two emerging fields of nature-inspired computing (NIC) and computational intelligence (CI) with focusing on the physics- and biology-based approaches and algorithms .Herein, by more details this article concerned with the conceptual analysis and evaluation of quantified learning creativity phenomenon via simulation and modeling of two diverse natural biological systems (human & non-human creatures). More precisely, it is associated to diverse aspects of measurable behavioral learning performance of both biological systems. Therefore, this paper introduces comparative analogy between two diverse biological behavioral systems considering quantification of learning creativity. Referring to, the definition of Swarm intelligence which considered as a relatively new discipline that deals with the study of self-organizing processes both in nature and in artificial systems. Researchers in ethology and animal behavior have proposed many models to explain interesting aspects of social insect behavior such as self-organization and shape-formation. Accordingly, the presented study observed during human interactive tutoring/learning processes with natural environment. Versus ecological behavioral learning of swarm intelligence agents (Ants), while performing foraging process . Systematic investigational study of quantified human learning creativity phenomenon is an interdisciplinary, challenging, and interesting educational issue. At education field practice (classrooms) , while face to face tutoring sessions are performed, learning creativity phenomenon is detectable via bidirectional feedback between teacher and pupil. In short, this research work adopts comparative study of simulation and modeling for educational creativity issue considering two disciplinary approaches are namely: swarm intelligence, and neural networks. Both simulated realistically for systematic investigational modeling of creatures' creativity phenomenon observed in nature. Presented creativity models mainly consider observed behavioral learning of ant colony system in addition to in field educational classrooms. Conclusively, presented results herein, for both swarm intelligence and neural networks models seemed to be well promising for future more elaborate, systematic, and innovative research in evaluation of human learning creativity phenomenon regarding (NIC) and (CI).

Improving multi-agent manufacturing control system by indirect communication based on ant agents

Manufacturing control systems are facing difficulty on adopting agility in today’s dynamic environment. Much research has been conducted on the use of multi-agent systems to facilitate this transition. In our previous work, a radio frequency identification–enabled multi-agent-based manufacturing control system was introduced for flexible manufacturing systems. In that control system, a direct coordination mechanism coordinates the agents. However, the decision-making response of the control system to disturbances was low and caused the agents to spend more time on processing messages than doing the actual job. Thus, in this article, we propose an indirect coordination mechanism based on ant colony intelligence aiming to improve the self-organization and processing time of the system. The new system utilizes the stigmergy mechanism for cooperation among the agents. Furthermore, ant agents are created by the existing agents to enable communication among them. The proposed new mechanism was implemented in a flexible assembly line. The simulation results of a case study considering shop floor problems indicates, for most performance measures, that the radio frequency identification–enabled multi-agent-based manufacturing control system integrated with indirect coordination mechanism performs better in comparison with the radio frequency identification–enabled multi-agent-based manufacturing control system integrated with direct coordination mechanism.

COMPUTATIONAL SCIENCE GUIDED SOFT COMPUTING BASED CRYPTOGRAPHIC TECHNIQUE USING ANT COLONY INTELLIGENCE FOR WIRELESS COMMUNICATION (ACICT)

In this paper, a computational science guided soft computing based cryptographic technique using Ant Colony Intelligence (ACICT) has been proposed. In this proposed approach at first a metamorphosed based strategy is used to produce intermediate cipher text. Finally, ACI generated keystream is used to further encrypt the intermediate cipher text to produce the final cipher text. In this approach an ant agent having a pheromone deposition consisting of a group of alphanumeric characters is called a key stream and each character in the key stream is known as key. The key stream length always be less than or equal to the plaintext to be encrypt. The keystream generation is based on distribution of characters in the plaintext. Instead of transmitting the plain keystream to the receiver, further encryption is done on keystream and encrypted keystream get transmitted to the receiver. Parametric tests are done and results are compared with some existing classical techniques, which show comparable results for the proposed system.

Ant Colony Intelligence in Flexible Manufacturing Scheduling Using Contract-Net Model

This paper builds an efficient agent-based flexible scheduling for real-world manufacturing systems. Considering the alternative processes and alternative machines, the allocation of manufacturing resources is achieved through negotiation among the job and machine agents in a multi-agent system (MAS). Ant Colony Intelligence (ACI) is proposed to be combined with Contract Net Protocol (CNP) so as to make agents adaptive to changing circumstances. ACI is integrated into both machine agents and job agents to solve the task allocation and sequencing problem. CNP is introduced to allow the agents to cooperate and coordinate their local schedules in order to find globally near-optimal robust schedules. The negotiation protocol is an interactive bidding mechanism based on the hybrid contract net protocol. The implementation of the issues using CNP model is discussed. Experimental results verify the effectiveness and efficiency of the proposed algorithm integrated with ant-inspired coordination.

Ant colony intelligence in multi-agent dynamic manufacturing scheduling

This study aims at building an efficient agent-based dynamic scheduling for real-world manufacturing systems with various products, processes, and disturbances. Ant colony intelligence (ACI) is proposed to be combined with local agent coordination so as to make autonomous agents adaptive to changing circumstances and to give rise to efficient global performance. The work here differs from other dynamic scheduling research in two areas: (1) a more generic and realistic manufacturing model with multiple product types, multiple/parallel multi-purpose machines with sequence-dependent setup constraints, and various dynamic disturbances is used, (2) ACI integrated with both machine agents and job agents to solve not only the task allocation problem, but also the task sequencing problem. The implementation of the aforementioned issues in a multi-agent system (MAS) is discussed. Simulation results show that, for most of the performance measures, a MAS integrated with well-designed ant-inspired coordination performs well compared to a MAS using dispatching rules.