An Enhanced Hunger Games Search Optimization with Application to Constrained Engineering Optimization Problems

Abstract

The Hunger Games Search (HGS) is an innovative optimizer that operates without relying on gradients and utilizes a population-based approach. It draws inspiration from the collaborative foraging activities observed in social animals in their natural habitats. However, despite its notable strengths, HGS is subject to limitations, including inadequate diversity, premature convergence, and susceptibility to local optima. To overcome these challenges, this study introduces two adjusted strategies to enhance the original HGS algorithm. The first adaptive strategy combines the Logarithmic Spiral (LS) technique with Opposition-based Learning (OBL), resulting in the LS-OBL approach. This strategy plays a pivotal role in reducing the search space and maintaining population diversity within HGS, effectively augmenting the algorithm's exploration capabilities. The second adaptive strategy, the dynamic Rosenbrock Method (RM), contributes to HGS by adjusting the search direction and step size. This adjustment enables HGS to escape from suboptimal solutions and enhances its convergence accuracy. Combined, these two strategies form the improved algorithm proposed in this study, referred to as RLHGS. To assess the efficacy of the introduced strategies, specific experiments are designed to evaluate the impact of LS-OBL and RM on enhancing HGS performance. The experimental results unequivocally demonstrate that integrating these two strategies significantly enhances the capabilities of HGS. Furthermore, RLHGS is compared against eight state-of-the-art algorithms using 23 well-established benchmark functions and the CEC2020 test suite. The experimental results consistently indicate that RLHGS outperforms the other algorithms, securing the top rank in both test suites. This compelling evidence substantiates the superior functionality and performance of RLHGS compared to its counterparts. Moreover, RLHGS is applied to address four constrained real-world engineering optimization problems. The final results underscore the effectiveness of RLHGS in tackling such problems, further supporting its value as an efficient optimization method.

Keywords: Hunger Games Search; Rosenbrock Method; benchmark; engineering optimization problems; logarithmic spiral; swarm intelligence.

Figure 1

Flowchart of RLHGS.

Figure 2

3-D map of some 23 classic benchmark functions. Different colors represent different solutions to the function, the mark of “F1”, “F2” etc. in the figure refer to the corresponding function.

Figure 3

(a) 3-D distributions of test functions, (b) 2-D position distribution of RLHGS, (c) RLHGS trajectories in the first dimension, (d) convergence curve of RLHGS. The line is generated by the projection of a three-dimensional figure. Different color represents different solution of function.

Figure 4

(a) balance analysis of the RLHGS, (b) balance analysis of the HGS, (c) convergence curves of RLHGS and HGS.

Figure 5

Convergence curves of RLHGS, RHGS, LHGS, and HGS on eight CEC2020 functions.

Figure 6

Friedman test results on 23 classic benchmark functions.

Figure 7

Convergence curves of compared algorithms on six classic benchmark functions.

Figure 8

Friedman test results on CEC2020.

Figure 9

Convergence curves of compared algorithms on four CEC2020 functions.

Figure 10

Structure of the tension/compression spring problem.

Figure 11

Shape of the welded beam design problem.

Figure 12

Components of the pressure vessel design problem.

Figure 13

Components of the 3-bar truss design problem.