GREPHRO: Nature-inspired optimization duo for Internet-of-Things

The optimization techniques usually work with the maximization or minimization of the problem to obtain the local loci or cumulative global loci. Two-dimensional bio-inspired optimization techniques face convexing problems towards a global solution and use an increased number of iterations. Besides, the duality principle considers the dual optimization aspects of the problems leading to a large duality gap of uncertain deviations and optimization errors between any prime solution and its dual solution. Moreover, several problems exist where one objective function requires maximization and another objective function requires minimization using the same set of parameters and some chaining of the feedback process. In such cases, we generally use two different optimization problems as per the best suit to the problem environment and obtain the different sub-solutions of the individual problems. This increases the complexity of the system and often deviates from the original optimal solution. We address these problems of dual optimization in our present work. In this paper, we introduce the first optimization duo model for computing services. To be specific, our proposed model is the first optimization model that works in a dual combined mode with maximization and minimization simultaneously to obtain a global optimum value or loci. We call our model GREen PHotosynthesis and Respiratory-based Optimization (GREPHRO). GREPHRO is primarily motivated by the observation of plants’ photosynthesis and respiratory processes, which work on the same set of environmental variables and have a chaining process. Further, our proposed nature-inspired GREPHRO can value the global optima or infimum point considering a single objective function serving as maximization and minimization combined. GREPHRO uses the Lagrange dual principle and non-linear parameters to obtain a linear solution for the infimum optima. We use a set of experiments on the GREPHRO model in the domain of Wireless Sensor Network (WSN)-based Internet of Thing (IoT) to derive the use case of our proposed work. The experimental results and the comparative analysis with the two of our previous works show that GREPHRO takes fewer iterations with more stability of the optimum solution. Moreover, the computational and memory complexities are also less. Therefore, GREPHRO is efficient and suitable for two-dimensional optimization problems in resource-constrained environments for IoTs.