###### Abstract  
Meta-heuristic algorithms are computational methods inspired by evolutionary processes, animal or plant behaviors, physical events, and other natural phenomena. Due to their success in solving optimization problems, meta-heuristic algorithms are widely used in the literature, leading to the development of novel variants. In this paper, new swarm-based meta-heuristic algorithms, called Improved Boosters Algorithm (IRA), Bipolar Boosters Algorithm (BRA), and Bipolar Improved Boosters Algorithm (BIRA), which are mainly based on Boosters Algorithm (RA), are presented. First, the new versions of RA (IRA, BRA, and BIRA) were compared in terms of performance, revealing that BIRA achieved significantly better results than the other variants. Then, the performance of the BIRA algorithm was compared with the performances of meta-heuristic algorithms widely used in the literature, Standard Genetic Algorithm (SGA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Cuckoo Search (CS), and Grey Wolf Optimizer (GWO), and thus, its success in the literature was tested. Moreover, RA was also included in this test to show that the new version, BIRA, is more successful than the previous one (RA). For all comparisons, 20 well-known benchmark optimization functions, 11 CEC2014 test functions, and 17 CEC2018 test functions, which are also in the CEC2020 test suite, were employed. To validate the significance of the results, Friedman and Wilcoxon Signed Rank statistical tests were conducted. In addition, three commonly used problems in the field of engineering were used to test the success of algorithms in real-life scenarios: pressure vessel, gear train, and tension/compression spring design. The results indicate that the proposed algorithm (BIRA) provides better performance compared to the other meta-heuristic algorithms. e 2020 2020 27 April 2020 27  
criteria are met; either the optimum solution is discovered or the maximum number of generations is reached. One of the widely used meta-heuristic algorithms in the literature are Evolutionary Algorithms (EAs). EAs generally includes the following intelligent strategies: selection, recombination, and mutation. Some of the most known EAs are Genetic Algorithms (GAs) [5], Differential Evolution (DE) [6], Memetic Algorithms (MAs) [7], and Biogeography-Based Optimizer (BBO) [8]. The fact that living creatures in nature generally form flocks has led researchers to develop Swarm-based Algorithms (SBA). Among these algorithms, perhaps the most well-known is Particle Swarm Optimization (PSO). PSO simulates the social behavior of bird flocks and fish swarms [9]. Each particle (potential solution) in PSO also possesses a velocity to explore the search space. Other renowned SBAs encompass Ant Colony Optimization (ACO) [10], Cuckoo Search (CS) [11], Artificial Bee Colony (ABC) [12], Firefly Algorithm (FFA) [13], and Grey Wolf Optimizer (GWO) [14]. Furthermore, researchers inspired by fundamental subjects that have an important place in sciences such as physics, chemistry, and astronomy have led to the emergence of new meta-heuristic algorithms. Some common examples include Simulated Annealing (SA) [15], Big-Bang Big-Crunch (BBBC) [16], Gravitational Search (GS) [17], Black Hole (BH) [18], and Momentum Search algorithm (MSA) [19]. Apart from the meta-heuristic algorithms mentioned above, there are also algorithms inspired by human behavior such as Tabu Search (TS) [20], Harmony Search (HS) [21], Imperialist Competitive Algorithm (ICA) [22], Teaching Learning Based Optimization (TLBO) [23], and Fireworks Algorithm (FWA) [24]. In meta-heuristic algorithms, the idea that the population will improve by selecting the best individual is prevalent. However, the idea of more towards the good can cause the algorithm to get stuck in the local and not be able to reach the optimum value, especially in challenging test functions. One way to get rid of this situation may be to adapt bipolar behavior to the algorithm.