RSLBO: Random Selected Leader Based Optimizer

Abstract

Designed optimization problems in different disciplines of science should be solved using appropriate techniques. Optimization algorithms are among the most effective and widely used methods in solving optimization problems that are able to provide suitable solutions for these problems. Innovation and scientific contribution of this article in designing a new optimizer called Random Selected Leader Based Optimizer (RSLBO) in order to be used in optimizing the objective functions of optimization problems and achieving the desired solutions. The main idea of the proposed RSLBO is to increase the search power using a random leader. In fact, instead of the algorithm population update relying on several specific members, such as the best member or the worst member, any ordinary member of the population can be a leader in guiding and updating the algorithm population. RSLBO is described, then mathematically modelled to be used in solving optimization problems. The main feature and advantage of the proposed RSLBO is that its implementation and relationships are very simple and understandable, and it also lacks control parameters. The performance of the proposed RSLBO is evaluated on a set of twenty-three standard functions. Also, in order to analyse the quality of the proposed RSLBO in providing appropriate solutions to optimization problems, the results obtained from the proposed algorithm are compared with eight well-known algorithms including Flow Direction Algorithm (FDA), Hide Object Game Optimizer (HOGO), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Teaching-Learning-Based Optimization (TLBO), Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). The results of optimization of objective functions indicate the high ability of the RSLBO to solve optimization problems and provide appropriate and acceptable solutions. Also, the analysis and comparison of the performance of the eight well-known optimization algorithms against the proposed RSLBO shows that the RSLBO has provided more appropriate solutions and is superior and much more competitive than the eight compared algorithms.

An Application of Hunter-Prey Optimization for Maximizing Photovoltaic Hosting Capacity Along with Multi-Objective Optimization in Radial Distribution Network

“…The optimal number of PVs allocation is compared by implementing teaching-learning-based optimization (TLBO) [29], bald eagle search (BES) [30], coyote optimization algorithm (COA) [31], and butterfly optimization algorithm (BOA) [32]. Based on 50 independent runs, the performance of HPO is and other algorithms are compared in Table 5.…”

“…The NFL theorem enables academics suggest innovative optimization algorithms or improve existing ones. Some of such recent algorithms are: mixed leader based optimizer (MLBO) [27], three influential members based optimizer (TIMBO) [28], darts game optimizer (DGO) [29], mixed best members based optimizer (MBMBO) [30], multi leader optimizer (MLO) [31], and random selected leader based optimizer (RSLBO) [32]. In this context, the following are the major contributions for this paper.…”

Aquila Optimizer Based Optimal Allocation of Soft Open Points for MultiObjective Operation in Electric Vehicles Integrated Active Distribution Networks

“…Thus, researchers are inspired to introduce various new optimization algorithms and/or improvements in the existing algorithms. In recent times, darts game optimizer (DGO) [20], three influential members based optimizer (TIMBO) [21], random selected leader based optimizer (RSLBO) [22], football game based optimization (FGBO) [23], puzzle optimization algorithm (POA) [24], ring toss game-based optimization (RTGBO) [25] are some of such meta-heuristic algorithms. In this aspect, we have introduced a simple and efficient Aquila optimizer (AO) [26], inspired by foraging behaviour and unique hunting skills of Aquila' bird, for solving the proposed multi-objective function and compared its effectiveness with other algorithms.…”

White Shark Optimizer for Multi-Objective Optimal Allocation of Photovoltaic Distribution Generation in Electrical Distribution Networks Considering Different Kinds of Load Models and Penetration Levels

“…The algorithms considered in the literature, on the other hand, have a number of shortcomings, such as slow convergence, being computationally expensive, and having difficulties maintaining the variety among the possible solutions. In recent times, mixed leader based optimizer (MLBO) [16], three influential members based optimizer (TIMBO) [17], darts game optimizer (DGO) [18], mixed best members based optimizer (MBMBO) [19], multi leader optimizer [20], random selected leader based optimizer [21], equilibrium optimizer (EO) [22], and white shark optimizer (WSO) [23], are some of such recent meta-heuristic algorithms. In these aspects, the following are the major contributions of this paper.…”