Abstract
This paper intends to formulate a new multi-objective inspired method, called Pancreas Hormones Method (PHM) for solving optimization problems. PHM is a population-based method, which based on biological nature of pancreas hormones in the maintenance of blood glucose level in the human body system. The adaptive blood glucose control system has provided useful alternatives and supplements to the types of optimization problems embodied in distributed systems. In this method, cell absorption of glucose is considered as a candidate solution; this happens when each cells' receptors in the human body bind with insulin granule, which allows utilizing glucose by a cell. The pancreas evaluates the fitness of all solutions by measure blood glucose level (BGL) in each iteration (secretion phases). Insulin granules (molecules) tend to target cells randomly and search for the optimal solutions which can get it by retard BGL to normal range. In each generation of the algorithm, the best solution is which can access the BGL to the balance point, whereas the other solutions are considered as a searcher of the search space. In this paper, PHM designed, and then it validated, tested on the bases of standard benchmarks and compared with the results of some successful algorithms. The results of PHM are promising.
Highlights
In daily life there exist many problems whose objective are to either maximize or minimize some value under some specific constraints such as load balancing in terms of maximizing quality of services (QoS) within cloud computing environment, and travelling salesman problem in case minimizing of trip route[1]
It was the motivation of this method, in an attempt to find a general method to solve optimization problems inspired by precise and optimal system that stems from the biological nature of pancreas hormones in the maintenance of blood glucose level (BGL) in the body system
The human body takes its need of glucose until it reaches with its cells to the optimum extent, and the rest of it which is in excess of normal limit of BGL is dealt with by taking it out the body in excretion E process so the equation becomes as follows: Figure 2: Pancreas Hormones Method (PHM)'s design (plasma glucose (G) passes continuously through the pancreas's glucose transporter.Pancreas secrete the appropriate hormone.Insulin granules (I) tend randomly to target cells
Summary
In daily life there exist many problems whose objective are to either maximize or minimize some value under some specific constraints such as load balancing in terms of maximizing quality of services (QoS) within cloud computing environment, and travelling salesman problem in case minimizing of trip route[1]. Modern optimization techniques start to demonstrate their power in dealing with hard optimization problems in robotics and automation: manufacturing cells formation, robot motion planning, worker scheduling, cell assignment, vehicle routing problem, assembly line balancing, shortest sequence planning, sensor placement, unmanned-aerial vehicles (UAV) communication relaying and multi-robot coordination [2]. Particle swarm optimization was developed based on the swarm behavior of birds, insects and fish [3], [4], while simulated annealing was based on the annealing process of metals[5] It was the motivation of this method, in an attempt to find a general method to solve optimization problems inspired by precise and optimal system that stems from the biological nature of pancreas hormones in the maintenance of BGL in the body system. In real it implicitly keeps cells glucose levels in normal range
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