In this paper, we have used a novel initialization strategy to improve Whale optimization algorithm (WOA), which is named as The Improved Whale Optimization Algorithm (IWOA). To evaluate the capability of the algorithm in terms of efficiency and performance, we have implemented it to solve thermal economic multi-objective optimization problems of Plate Fin Heat Exchanger (PFHE). We have investigated the design problem with a single-objective as well as multi-objectives. In single-objective we have minimized the total cost and maximized the effectiveness of PFHE. In multi-objective, we have combined the total cost and effectiveness, with the help of design weights and a penalty parameter. The sensitivity of IWOA is checked towards the change in population sizes and the target prey numbers. The algorithm was stable in calculating the best values but was variative in number of functions evaluations. The performance of IWOA is compared with Genetic Algorithm (GA), Elitist-Jaya Algorithm (EJA), and modified-TLBO (Teaching Learning Based Optimization). Which show that IWOA has significantly improved the results. The suggested algorithm has less parameters to be set by designers. It converges to the required results quickly and is easy to implement. Similarly, all the experiments suggested that IWOA is applicable to design problems with complex objectives and highly non-linear constraints.