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An Enhanced Grey Wolf Optimizer with Elite Inheritance and Balance Search Mechanisms (2404.06524v1)

Published 9 Apr 2024 in cs.NE and cs.AI

Abstract: The Grey Wolf Optimizer (GWO) is recognized as a novel meta-heuristic algorithm inspired by the social leadership hierarchy and hunting mechanism of grey wolves. It is well-known for its simple parameter setting, fast convergence speed, and strong optimization capability. In the original GWO, there are two significant design flaws in its fundamental optimization mechanisms. Problem (1): the algorithm fails to inherit from elite positions from the last iteration when generating the next positions of the wolf population, potentially leading to suboptimal solutions. Problem (2): the positions of the population are updated based on the central position of the three leading wolves (alpha, beta, delta), without a balanced mechanism between local and global search. To tackle these problems, an enhanced Grey Wolf Optimizer with Elite Inheritance Mechanism and Balance Search Mechanism, named as EBGWO, is proposed to improve the effectiveness of the position updating and the quality of the convergence solutions. The IEEE CEC 2014 benchmark functions suite and a series of simulation tests are employed to evaluate the performance of the proposed algorithm. The simulation tests involve a comparative study between EBGWO, three GWO variants, GWO and two well-known meta-heuristic algorithms. The experimental results demonstrate that the proposed EBGWO algorithm outperforms other meta-heuristic algorithms in both accuracy and convergence speed. Three engineering optimization problems are adopted to prove its capability in processing real-world problems. The results indicate that the proposed EBGWO outperforms several popular algorithms.

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References (44)
  1. Amir, H. G. (2014). Interior Search Algorithm (ISA): A novel approach for global optimization. ISA Transactions, 53, 1168–1183.
  2. Social Network Search for solving engineering optimization problems. Computational Intelligence and Neuroscience, 2021.
  3. Callahan, J. J. (2010). Advanced Calculus: A Geometric View. Springer.
  4. Coello, C. A. C. (2000). Use of a self-adaptive penalty approach for engineering optimization problems. Computers in Industry, 41, 113–127.
  5. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1, 3–18.
  6. Selective opposition based grey wolf optimization. Expert Systems with Applications, 151, 113389.
  7. Dhiman, G. (2021). SSC: A hybrid nature-inspired meta-heuristic optimization algorithm for engineering applications. Knowledge-Based Systems, 222, 106926.
  8. A hybrid grey wolf-bat algorithm for global optimization. Advances in Intelligent Systems and Computing, 723, 3–12.
  9. Grey Wolf Optimizer: A review of recent variants and applications. Neural Computing and Applications, 30, 413–435.
  10. Gharehchopogh, F. S. (2022). Advances in Tree Seed Algorithm: A Comprehensive Survey. Archives of Computational Methods in Engineering, 29, 3281–3304.
  11. Load frequency control of interconnected power system using grey wolf optimization. Swarm and Evolutionary Computation, 27, 97–115.
  12. A novel random walk grey wolf optimizer. Swarm and Evolutionary Computation, 44, 101–112.
  13. Enhancing tree-seed algorithm via feed-back mechanism for optimizing continuous problems. Applied Soft Computing Journal, 92, 106314.
  14. DSGWO: An improved grey wolf optimizer with diversity enhanced strategy based on group-stage competition and balance mechanisms. Knowledge-Based Systems, 250, 109100.
  15. An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. Journal of Mechanical Design, 116, 405–411.
  16. Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90, 103541.
  17. Particle swarm optimization. Proceedings of ICNN’95-International Conference on Neural Networks, 4, 1942–1948.
  18. Kiran, M. S. (2015). TSA: Tree-Seed Algorithm for continuous optimization. Expert Systems with Applications, 42, 6686–6698.
  19. Chaotic grey wolf optimization algorithm for constrained optimization problems. Journal of Computational Design and Engineering, 5, 458–472.
  20. Learning-based elephant herding optimization algorithm for solving numerical optimization problems. Knowledge-Based Systems, 195, 105675.
  21. Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Technical Report.
  22. Echo state network optimization using binary grey wolf algorithm. Neurocomputing, 385, 310–318.
  23. A modified augmented Lagrangian with improved grey wolf optimization to constrained optimization problems. Neural Computing and Applications, 28, 421–438.
  24. Genetic Algorithms: concepts and applications in engineering design. IEEE Transactions on Industrial Electronics, 43, 519–534.
  25. AGWO: Advanced GWO in multi-layer perception optimization. Expert Systems with Applications, 173, 114676.
  26. Mirjalili, S. (2016). SCA: A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120–133.
  27. The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.
  28. Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61.
  29. Modified grey wolf optimizer for global engineering optimization. Applied Computational Intelligence and Soft Computing, 2016, 1–16.
  30. An improved grey wolf optimizer for solving engineering problems. Expert Systems with Applications, 166, 113917.
  31. MTDE: An effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems. Applied Soft Computing, 97, 106761.
  32. A random walk grey wolf optimizer based on dispersion factor for feature selection on chronic disease prediction. Expert Systems with Applications, 206, 117864.
  33. Rajabioun, R. (2011). Cuckoo Optimization Algorithm. Applied Soft Computing Journal, 11, 5508–5518.
  34. Gsa: A gravitational search algorithm. Information Sciences, 179, 2232–2248.
  35. Sandgren, E. (1990). Nonlinear integer and discrete programming in mechanical design optimization. Journal of Mechanical Design, 112, 223–229.
  36. Evolutionary population dynamics and grey wolf optimizer. Neural Computing and Applications, 26, 1257–1263.
  37. I-GWO and Ex-GWO: improved algorithms of the grey wolf optimizer to solve global optimization problems. Engineering with Computers, 37, 509–532.
  38. A new hybrid whale optimizer algorithm with mean strategy of grey wolf optimizer for global optimization. Mathematical and Computational Applications, 23, 14.
  39. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–359.
  40. Threshold binary grey wolf optimizer based on multi-elite interaction for feature selection. IEEE Access, 11, 34332–34348.
  41. Grouped grey wolf optimizer for maximum power point tracking of doubly-fed induction generator based wind turbine. Energy Conversion and Management, 133, 427–443.
  42. Ensemble grey wolf optimizer and its application for image segmentation. Expert Systems with Applications, 209, 118267.
  43. Opposition-based learning grey wolf optimizer for global optimization. Knowledge-Based Systems, 226, 107139.
  44. A shuffled cellular evolutionary grey wolf optimizer for flexible job shop scheduling problem with tree-structure job precedence constraints. Applied Soft Computing, 125, 109235.
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Authors (4)
  1. Jianhua Jiang (10 papers)
  2. Ziying Zhao (2 papers)
  3. Weihua Li (43 papers)
  4. Keqin Li (61 papers)

Summary

An Enhanced Grey Wolf Optimizer with Elite Inheritance and Balance Search Mechanisms

The paper "An Enhanced Grey Wolf Optimizer with Elite Inheritance and Balance Search Mechanisms" presents a novel version of the Grey Wolf Optimizer (GWO), termed Enhanced Balance Grey Wolf Optimizer (EBGWO). This work addresses two critical weaknesses in the original GWO algorithm: the absence of an elite inheritance mechanism and the lack of a balanced search guidance between the local and global search processes.

Enhancements in EBGWO

  1. Elite Inheritance Mechanism: The authors introduce an elite inheritance mechanism designed to significantly improve convergence effectiveness. This mechanism ensures that optimal solutions or 'elite' positions identified during previous iterations are retained and utilized in subsequent iterations. This addresses the issue, present in the original GWO, where the algorithm might overlook high-quality solutions it previously discovered. Inspired by concepts from genetic algorithms, the proposed elite inheritance mechanism captures high-performing individuals to guide future search directions, thereby enhancing solution accuracy.
  2. Balance Search Mechanism: To counteract the GWO's inclination towards local optima due to insufficient exploration, a balance search mechanism is introduced. This mechanism includes a Search Tendency (ST) operator that dynamically balances exploration and exploitation. The selection of guiding wolves at each step is diversified by employing a combination of elite and random wolves, which expands the search space and improves the chances of escaping local optima.

Methodology and Experiments

The efficacy of the EBGWO algorithm is tested against the IEEE CEC 2014 benchmark functions suite, a widely accepted collection of test functions for optimization algorithms. Various dimensions (10, 30, 50, and 100D) were chosen for a comprehensive performance analysis. The results demonstrated that EBGWO outperforms the original GWO and several of its variants in terms of convergence speed and accuracy.

Quantitative analyses within the paper highlight EBGWO's superior ability to maintain a balance between exploration and exploitation, effectively escaping local optima while converging swiftly to global solutions. The numerical results are compelling, with EBGWO showing consistently lower mean values and standard deviations across numerous test functions in comparison to baseline algorithms like standard GWO, SOGWO, mGWO, AGWO, SCA, and WOA.

Implications and Future Work

This paper's findings underline the potential for enhancing GWO with mechanisms inspired by evolutionary algorithms to cater to its inherent weaknesses. Practically, the improved algorithm is applicable in engineering problem-solving, where solution quality and computational efficiency are paramount. Three engineering design problems were successfully tackled using EBGWO, exhibiting its robustness and versatility in real-world scenarios.

Theoretical implications suggest that further exploration of elite retention and diversified guiding strategies could yield fruitful developments in meta-heuristic algorithms. Future work could potentially explore modifications to the hierarchy structure of the wolf pack or extend the algorithm’s capabilities to tackle large-scale and multi-objective optimization problems.

Conclusion

This paper provides a substantial improvement to the Grey Wolf Optimizer through the introduction of the elite inheritance and balance search mechanisms. EBGWO demonstrates a strong capacity to improve solution accuracy and convergence speed, underpinning its suitability for a wide range of complex optimization tasks. The integration of elite retention strategies and balanced search explorations present valuable advancements in the meta-heuristic optimization landscape.

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