Hybrid leader based optimization: a new stochastic optimization algorithm for solving optimization applications

Abstract

In this paper, a new optimization algorithm called hybrid leader-based optimization (HLBO) is introduced that is applicable in optimization challenges. The main idea of HLBO is to guide the algorithm population under the guidance of a hybrid leader. The stages of HLBO are modeled mathematically in two phases of exploration and exploitation. The efficiency of HLBO in optimization is tested by finding solutions to twenty-three standard benchmark functions of different types of unimodal and multimodal. The optimization results of unimodal functions indicate the high exploitation ability of HLBO in local search for better convergence to global optimal, while the optimization results of multimodal functions show the high exploration ability of HLBO in global search to accurately scan different areas of search space. In addition, the performance of HLBO on solving IEEE CEC 2017 benchmark functions including thirty objective functions is evaluated. The optimization results show the efficiency of HLBO in handling complex objective functions. The quality of the results obtained from HLBO is compared with the results of ten well-known algorithms. The simulation results show the superiority of HLBO in convergence to the global solution as well as the passage of optimally localized areas of the search space compared to ten competing algorithms. In addition, the implementation of HLBO on four engineering design issues demonstrates the applicability of HLBO in real-world problem solving.

Guided Pelican Algorithm

“…Then, each solution conducts neighborhood search where the local search space is reduced linearly during the iteration [23]. In hybrid leader-based optimization (HLBO), a leader of each solution is constructed from the normalized quality of three solutions: the related solution, the highest quality solution, and the randomly selected solution [24]. The normalized quality of each entity defines the portion of this entity to construct the hybrid leader.…”

“…The normalized quality of each entity defines the portion of this entity to construct the hybrid leader. Then, each solution will follow its own hybrid leader [24]. In the mixed leader-based optimizer, there are two types of leaders [25].…”

Total Interaction Algorithm: A Metaheuristic in which Each Agent Interacts with All Other Agents

“…The result indicates that TIA is superior to other benchmark metaheuristics, especially in solving the high dimension functions. TIA is better than PSO, MPA, GSO, GPA, and DTBO in 22,21,16,11,13 functions. The result also indicates that the increase of the maximum iteration improves the performance of TIA mostly in solving high dimension unimodal functions.…”

“…Some metaheuristics were built based on the game mechanics, such ring toss game-based optimizer (RTGBO) [17], darts game optimizer (DGO) [18], and puzzle optimization algorithm (POA) [19]. Some metaheuristics used term leader as the inspiration, such as random selected leader-based optimizer (RSLBO) [20], hybrid leader-based optimizer (HLBO) [21], mixed leader-based optimizer (MLBO) [22], multi leader optimizer (MLO) [23], and so on.…”

“…In some metaheuristics, such as DTBO [15] and NGO [11], each agent interacts with a randomly selected agent. In some metaheuristics, such as HLBO [21] and MLBO [22], each agent interacts with the combination between the highest quality agent and randomly selected agent. Meanwhile, metaheuristic in which each agent interacts with all other agents is hard to find.…”