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. 2023 Aug 23;8(5):383.
doi: 10.3390/biomimetics8050383.

Adaptive Guided Equilibrium Optimizer with Spiral Search Mechanism to Solve Global Optimization Problems

Hongwei Ding  1 , Yuting Liu  1 , Zongshan Wang  1 , Gushen Jin  2 , Peng Hu  3 , Gaurav Dhiman  4
Affiliations

Adaptive Guided Equilibrium Optimizer with Spiral Search Mechanism to Solve Global Optimization Problems

Hongwei Ding et al. Biomimetics (Basel). .

Abstract

The equilibrium optimizer (EO) is a recently developed physics-based optimization technique for complex optimization problems. Although the algorithm shows excellent exploitation capability, it still has some drawbacks, such as the tendency to fall into local optima and poor population diversity. To address these shortcomings, an enhanced EO algorithm is proposed in this paper. First, a spiral search mechanism is introduced to guide the particles to more promising search regions. Then, a new inertia weight factor is employed to mitigate the oscillation phenomena of particles. To evaluate the effectiveness of the proposed algorithm, it has been tested on the CEC2017 test suite and the mobile robot path planning (MRPP) problem and compared with some advanced metaheuristic techniques. The experimental results demonstrate that our improved EO algorithm outperforms the comparison methods in solving both numerical optimization problems and practical problems. Overall, the developed EO variant has good robustness and stability and can be considered as a promising optimization tool.

Keywords: equilibrium optimizer; global optimization; metaheuristics; mobile robot path planning; nature-inspired.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Nonlinear decay of the proposed inertia weight.
Figure 2
Figure 2
The flowchart of SSEO.
Figure 3
Figure 3
Friedman mean ranks obtained by the employed algorithms on CEC 2017 benchmark functions with 30 dimensions.
Figure 4
Figure 4
Friedman mean ranks obtained by the employed algorithms on CEC 2017 benchmark functions with 100 dimensions.
Figure 5
Figure 5
Map 1: (a) ABC, (b) FA, (c) GWO, (d) PSO, (e) SSA, and (f) SSEO.
Figure 6
Figure 6
Map 2: (a) ABC, (b) FA, (c) GWO, (d) PSO, (e) SSA, and (f) SSEO.
Figure 7
Figure 7
Map 3: (a) ABC, (b) FA, (c) GWO, (d) PSO, (e) SSA, and (f) SSEO.
Figure 8
Figure 8
Map 4: (a) ABC, (b) FA, (c) GWO, (d) PSO, (e) SSA, and (f) SSEO.
Figure 9
Figure 9
Map 5: (a) ABC, (b) FA, (c) GWO, (d) PSO, (e) SSA, and (f) SSEO.
See this image and copyright information in PMC

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