Abstract
In the digital world of medical transcription involving various dimensions of processes, detecting the edge of a standard medical image for clinical research/diagnosis, telemedicine and other applicative purposes requires various efficient and effective methodologies to address the needs of the processes. Among these various meta-heuristics, as the size of the problem tends to increase along with time, the processes and their elemental techniques, proven to have been providing viable solutions appeals for reserve management and lesser computation times, with the efficiency of such algorithms and algorithmic operations to be enhanced at suitable levels of abstraction. 
 In this paper we propose an effective topological algorithm, which inhibits the characteristic features of high performance parallel enumeration in such heterogeneous computation environments. The proposed scheduler in the defined topological algorithm takes into consideration the metrics generated by As Built Critical Path (ABCP) - A hybrid methodological process. These metrics are re-initialized and processed to address the management of resources and the realization of search space. We also propose a methodology for shared memory access by the ants to perform parallel computation and as well implement the optimization factor in detecting the edge. An in-depth analysis with respect to the Speedup factor and the Execution time metrics are analyzed for various scenarios under consideration. The differentiations are evaluated and plotted for further futuristic analysis
Highlights
The parallelization algorithms and their solutions, has broadened the exquisiteness of the parallel architectures involving a broad spectrum of parallel based algorithms supporting Ant Colony Optimization (ACO) techniques
The most important approach that mostly follows the inadvertent parallel and multiple ant colonial approaches, were and significantly dedicated by the reactionary hardware-adapted parallel approaches. This was proposed by Scheuermann et al[13], with the designs being implemented with parallel ACO algorithms or Field Programmable Gate Arrays (FPGA)
Their work initialized the Ant Colony Optimization algorithm used to solve a class of NP-hard combinatorial optimization problem such as Quadrature Assignment Problem (QAP), having a vibrant applicative features such as image synthesis, data analysis, etc
Summary
The parallelization algorithms and their solutions, has broadened the exquisiteness of the parallel architectures involving a broad spectrum of parallel based algorithms supporting ACO techniques. These underlying tasks are shown to be "Non-deterministic Polynomial" NP-complete with NP-hard requisites in various different requiring scenarios One such problem was addressed using a novel framework in scheduling the tasks based on the meta-heuristic process of ACO. Fang Liu[5], proposed an inference to the property of torpid and accomplishment of the concept of evolution in popular heuristic ACO algorithm, a dual population ACO involving parallelism (DPPACO) This algorithm was successfully tested and applied to the preeminent travelling salesman problem. The most important approach that mostly follows the inadvertent parallel and multiple ant colonial approaches, were and significantly dedicated by the reactionary hardware-adapted parallel approaches This was proposed by Scheuermann et al[13], with the designs being implemented with parallel ACO algorithms or Field Programmable Gate Arrays (FPGA). Their work initialized the Ant Colony Optimization algorithm used to solve a class of NP-hard combinatorial optimization problem such as Quadrature Assignment Problem (QAP), having a vibrant applicative features such as image synthesis, data analysis, etc
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