## 1 Introduction  
JADE (For the reader's convenience, please find and refer to the full list of all abbreviations adopted in this paper, at the very end, right before the bibliography section) is a crucial operation in many digital receivers. Highly accurate and computationally inexpensive JADE is required in many fields ranging from military applications such as RADAR or SONAR systems to wireless indoor positioning [1, 2] and wireless communication systems [3]. Moreover, the estimation of AoAs and TDs enable the design of highly accurate localization techniques [4]. Many existing works have focused on solving the JADE problem. Most fall under the subspace-based category such as Multiple Signal Classification (MUSIC) [5], ESPRIT [6], and the 2D unitary matrix pencil (UMP) [7]. One of the iterative ML estimators based on the space-alternating generalized expectation maximization (SAGE) algorithm was proposed in [8]. The approach in [9] mainly targets only the DOA estimation. It introduces a DOA estimation algorithm in a full-dimension MIMO system by solving the maximum likelihood estimation using expectation-maximization (EM)algorithm. Recently, a tensor-based approach for channel and target parameter estimation was proposed in [10]. Also, a non-iterative ML estimator that tackles the JADE problem in a multi-carrier transmission context was developed in [11] using the IS technique. More recently, we proposed a new ML JADE solution in a non-DA (NDA) single-carrier scenario [12]. Shortly after, we have tackled the same scenario by making IS initialize the differential evolution (DE) technique in [13]. Referred to hereafter as IS-DE, it was a first attempt to tackle JADE by exploiting a bio-inspired optimization approach. Under this category, GWO has notably been used to optimize and solve many engineering problems. The best-known applications are numerical simulations and stability fields [14; 15], feature acquisition selection, dataset classification, neural networks training [16], etc. A multi-objective GWO was developed for cloud computing in [17], and for wireless sensor networks in [18]. It was adapted to a multi-robot application in [19], and to unmanned aerial vehicles (UAVs) in [20]. A GWO-based optimal channel estimation technique was proposed for large-scale MIMO in LTE networks in [21]. A dragonfly-evaluated gray wolf optimization (DA-GWO) model was introduced in [22], which hybridizes the concepts of dragonfly algorithm and GWO for channel estimation in millimeter wave massive MIMO system. In [23], we find also that the GWO algorithm was used only for direction of arrival (DoA) estimation. Nevertheless, GWO suffers from slow convergence, limited solution accuracy, and susceptibility to getting trapped in local optima. Many improvements to GWO were proposed in different applications [24; 25; 26; 27], but none were to tackle JADE, to the best of our knowledge. In this paper, we exploit GWO [28] to solve JADE over OFDM SIMO transmissions in multi-path environments. The main idea consists of improving the GWO by initializing the wolf positions using the IS technique instead of random positions. More importantly, and beyond simple initialization of GWO with IS (coined as IS-GWO hereafter), we modify and dynamically update the conventional simple expression for the convergence factor of the GWO algorithm that entirely drives its hunting and tracking mechanisms by accounting for new CDFs derived from the IS technique. Numerical assessments will confirm the advantages of the proposed GWOEIS over IS, GWO, IS-GWO, DE, IS-DE, and other state-of-the-art JADE solutions in terms of estimation accuracy, population or sample size (e.g., number of wolves), global convergence, and convergence speed. The remainder of this paper is organized as follows. Section 2 introduces the multi-carrier SIMO system model in multi-path environments.