Nature inspired swarm based meta-heuristic optimization technique is getting considerable attention and established to be very competitive with evolution based and physical based algorithms. This paper proposes a novel Buyer Inspired Meta-heuristic optimization Algorithm (BIMA) inspired form the social behaviour of human being in searching and bargaining for products. In BIMA, exploration and exploitation are achieved through shop to shop hoping and bargaining for products to be purchased based on cost, quality of the product, choice and distance to the shop. Comprehensive simulations are performed on 23 standard mathematical and CEC2017 benchmark functions and 3 engineering problems. An exhaustive comparative analysis with other algorithms is done by performing 30 independent runs and comparing the mean, standard deviation as well as by performing statistical test. The results showed significant improvement in terms of optimum value, convergence speed, and is also statistically more significant in comparison to most of the reported popular algorithms.
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