International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-6, March 2020 4843 Retrieval Number: F8189038620/2020©BEIESP DOI:10.35940/ijrte.F8189.038620 Published By: Blue Eyes Intelligence Engineering & Sciences Publication An Extended Artificial Neural Network Assisted Hybrid Harris Hawks and Whale Optimizer to Find Optimal Solution for Engineering Design Problems Someet Singh, Anuj Jain, Sunil Kumar Mahla Abstract: The algorithms that have been developed recently have decorous behavior to solve and find optimum solution to various optimization problems in search space. Withal such calculations stuck in issues nearby quest space for compelled engineering problems. In succession to achieve an optimal solution a hybrid algorithmic approach is proffered. Artificial Neural Network (ANN) is considered as better solution for the known outputs. A hybrid variant of applying ANN on Harris Hawks and Whale Optimization Algorithm (ANNHHOWOA) is proposed to achieve effective solution for engineering problems. The effectiveness of proposed algorithm is tested for various non- linear, non-convex and standard engineering problems and to approve consequences of proposed algorithm standard benchmarks and multidisciplinary design problems have been considered. The validation endorsed that the results shown by ANNHHOWOA showed much better results than individual ANN, HHO and WOA and its effectiveness on multidisciplinary engineering problems. Keywords: meta-heuristic algorithms, artificial neural network. I. INTRODUCTION Implementation and use of machine learning and artificial intelligence has become popular and has been accepted as best techniques over the last decade. Machine learning and artificial intelligence are being widely used over the years and provide efficient solutions to solve real world, discrete, constrained or unconstrained, linear or nor linear engineering problems. The continuous research and the results produced from the experiments done in this field has resulted that the available methods such as sequential quadratic programming, quasi-Newton method has not shown significant and effective behavior to find solutions for non-continuous, multi-model problems. So, meta- heuristic algorithms have been taken into consideration to get accurate and efficient result of the defined problems. Meta-algorithms have been implemented to achieve efficient results for the natural, multi-modal and engineering problems. Machine learning is one of the most exciting recent technologies in Artificial Intelligence. Heuristics are strategies to discover great ideal arrangements in a computational expense without ensuring achievability or optimality. Revised Manuscript Received on March 30, 2020. Someet Singh, School of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India Anuj Jain, School of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India Sunil Kumar Mahla, Department of Mechanical Engineering, IKJPTU, Punjab, India Neural systems are demonstrating strategies equipped for displaying complex functions. Artificial neural networks are naturally propelled simulations performed on the workstation to perform particular tasks such as clustering, classification, pattern recognition, statistical analysis and data modeling. A neural system is an interrelated assemblage of basic processing components or nodes, whose purpose is inexactly in view of human neuron. Those transforming capacity of the organize may be saved in the inter-unit association strengths, alternately weights, gotten toward a transform from claiming adjustment to, or taking in from, an set about preparation designs. Neural Networks present an efficient approach to compute and understand the working of human brain. It takes numerous sources of info having various weightings and has one yield which relies upon the characterized inputs. Neural network is broadly utilized as a result of its capacity to sum up and to react to unpredicted inputs. A neural network does not need to be reprogrammed as it learns itself. Throughout training, neurons are taught to distinguish different particular designs what's more if on start alternately not at that design may be gained. A. Feedforward Neural Network A feed forward neural network is an artificial neural system wherein associations among the hubs don't prompt development of cycle. In feed forward network the progression of data is unidirectional, forward from the information hubs to the yield nodes through concealed layers. B. Basic Architecture Neural Network layers are autonomous and can have any number of hubs. Count of concealed hubs must be greater than all input hubs. The network must contain at least one hidden layer. Value of bias nodes is always set to 1. Figure 1: General Architecture