each antenna element is equal to a fraction of wavelength of the received signal. To detect more targets using ULA, more antenna elements will be needed, which will increase the cost of hardware and computing complexity. This limitation in ULAs leads us to the world of sparse arrays. Minimum redundancy array (MRA) [11], nested array (NA) [12,13] and coprime sensor arrays (CSA) [14,15] are the most popular types of the sparse arrays. These spare arrays are used to attain more number of degree of freedom rather than N-1 targets with N sensors. In sparse arrays, distance between each antenna elements is not same. MRA has maximum \(N(N-1)+1\) DOF but it has no closed form expression for its array structure. A nested array has \(O(N^{2})\) DOF but it has more mutual coupling issue than MRA and coprime due to a dense ULA in its physical structure. Although, CSA has more holes in its co-array structure but it has less mutual coupling and additionally, \(O(MN)\) is the DOF for this array structure. The past decade has been shown a huge growth in CSAs. Consequently, CSA is used in this paper due to enhanced DOF and reduced mutual coupling effect for efficient detection of targets in far field. CSA is obtained from union of two ULAs of coprime numbers. The second aspect of the DOA estimation is categorized into two further parts. First are the deterministic algorithms and the second part is the heuristic algorithms. Deterministic algorithms are sub-space based and consist of multiple signal classification(MUSIC) [16], root MUSIC [17], estimation of signal parameters via rotational invariance techniques (ESPRIT) [18], minimum variance distortionless response (MVDR) [19], maximum Likelihood (ML) [20], etc. Although, these sub-space methods perform well and provide better resolution [21,22] in terms of computational complexity, solving a problem quickly and increasing robustness to also increase efficiency, the degree of freedom heuristic algorithms are employed. In consonance with the literature, sub-space based methods are eligible to estimate \(N-1\) targets by configuring N antenna elements. Therefore, heuristic algorithms are used to subdue this issue. Numerous algorithms are included under heuristic algorithms, such as Genetic Algorithm (GA) [23], flower pollination algorithm (FPA) [24], particle swarm optimization (PSO) [25,26], ant colony optimization (ACO) [27], bee colony optimization (BCO) [28], simulated annealing (SA) [29], differential evolution (DE) [30], etc. For far-field narrow-band signal DOA estimation, so far, a set of algorithms have been proposed, such as Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), Multiple Signal Classification (MUSIC), Maximum Likelihood (ML, including Deterministic ML (DML) and Stochastic ML (SML), compressed sensing, and Weighted Subspace Fitting (WSF). Among these algorithms, it is well known that ML and WSF have the highest accuracy of DOA estimation. However, it is also well demonstrated that the computational complexity of them is also the highest because their criteria are nonlinear multi-dimensional optimization [31]. In this research work, we exploit the features of Flower Pollination Algorithm and estimate the direction of arrival. FPA is a biological-inspired soft computing algorithm, which is associated with the transformation of pollens by pollinators like bees, birds and other insects. There are two basic procedures for transformation of pollens. The first one is biotic and the other is an abiotic process. Biotic or cross-pollination is also known as global search pollination and abiotic or self- pollination is also known as local search pollination. In FPA the major objective is to deduce the best reproduction of plants by surviving the fittest flower in the flowering plants [32]. The aim of our research is to estimate DOA through novel computing paradigm using coprime sensor array with the help of meta heuristic technique.