Hybrid Artificial Root Foraging Optimizer Based Multilevel Threshold for Image Segmentation

This paper proposes a new plant-inspired optimization algorithm for multilevel threshold image segmentation, namely, hybrid artificial root foraging optimizer (HARFO), which essentially mimics the iterative root foraging behaviors. In this algorithm the new growth operators of branching, regrowing, and shrinkage are initially designed to optimize continuous space search by combining root-to-root communication and coevolution mechanism. With the auxin-regulated scheme, various root growth operators are guided systematically. With root-to-root communication, individuals exchange information in different efficient topologies, which essentially improve the exploration ability. With coevolution mechanism, the hierarchical spatial population driven by evolutionary pressure of multiple subpopulations is structured, which ensure that the diversity of root population is well maintained. The comparative results on a suit of benchmarks show the superiority of the proposed algorithm. Finally, the proposed HARFO algorithm is applied to handle the complex image segmentation problem based on multilevel threshold. Computational results of this approach on a set of tested images show the outperformance of the proposed algorithm in terms of optimization accuracy computation efficiency

Artificial intelligence and multiple criteria decision making approach for a cost-effective RFID-enabled tracking management system

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe implementation of RFID technology has been subject to ever-increasing popularity in relation to the traceability of items as one of the most advance technologies. Implementing such a technology leads to an increase in the visibility management of products. Notwithstanding this, RFID communication performance is potentially greatly affected by interference between the RFID devices. It is also subject to auxiliary costs in investment that should be considered. Hence, seeking a cost-effective design with a desired communication performance for RFID-enabled systems has become a key factor in order to be competitive in today‟s markets. This study introduce a cost and performance-effective design for a proposed RFID-enabled passport tracking system through the development of a multi-objective model that takes in account economic, operation and social criteria. The developed model is aimed at solving the design problem by (i) allocating the optimal numbers of related facilities that should be established and (ii) obtaining trade-offs among three objectives: minimising implementation and operational costs; minimising RFID reader interference; and maximising the social impact measured in the number of created jobs. To come closer to the actual design in terms of considering the uncertain parameters, a fuzzy multi-objective model was developed. To solve the multi-objective optimization problem model, two solution methods were used respectively (epsilon constrain and linear programming) to select the best Pareto solution and a decision-making method was developed to select the final trade-off solution. Moreover, this research aims to provide a user-friendly decision making tool for selecting the best vendor from a group which submitted their tenders for implementing a proposed RFID- based passport tracking system. In addition to that a real case study was applied to examine the applicability of the developed model and the proposed solution methods. The research findings indicate that the developed model is capable of presenting a design for an RFID- enabled passport tracking system. Also, the developed decision-making tool can easily be used to solve similar vendor selection problem. Research findings demonstrate that the proposed RFID-enabled monitoring system for the passport tracking system is economically feasible. The study concludes that the developed mathematical models and optimization approaches can be a useful decision-maker for tackling a number of design and optimization problems for RFID system using artificial intelligence mathematical algorithm based techniques

Cooperative artificial bee colony algorithm for multi-objective RFID network planning

Radio frequency identification (RFID) is rapidly growing into an important technology for object identification and tracking applications. This gives rise to the most challenging RFID network planning (RNP) problem in the large-scale RFID deployment environment. RNP has been proven to be an NP-hard problem that involves many objectives and constraints. The application of evolutionary and swarm intelligence algorithms for solving multi-objective RNP (MORNP) has gained significant attention in the literature, while these proposed methods always transform multi-objective RNP into single-objective problem by the weighted coefficient approach. In this work, we propose a cooperative multi-objective artificial colony algorithm called CMOABC to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP. The experiment presents an exhaustive comparison of the proposed CMOABC and two successful multi-objective techniques, namely the recently developed multi-objective artificial bee colony algorithm (MOABC) and nondominated sorting genetic algorithm II (NSGA-II), on instances of different nature, namely the two-objective and three-objective MORNP in the large-scale RFID scenario. Simulation results show that CMOABC proves to be superior for planning RFID networks compared to NSGA-II and MOABC in terms of optimization accuracy and computation robustness