A Novel Memetic Algorithm Based on Multiparent Evolution and Adaptive Local Search for Large-Scale Global Optimization

Zhang, Wenfen; Lan, Yulin

doi:10.1155/2022/3558385

Cited by 3 publications

(2 citation statements)

References 52 publications

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“…An adaptive LS method is used for exploitation purposes locally. in the beginning, the LS and global search (GS) are performed alternatively and the population size is reduced to one, but in the end stages, the LS is performed for the search of the last individuals [35]. An LS based on the Hadamard matrix is used to enhance the performance and improve searchability [36] with an enhanced probability of finding a feasible optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Quantum Inspired Evolutionary Algorithm with a Novel Elitist Local Search Method for Scheduling of Thermal Units

Singh

Mani

Singh

et al. 2023

IJRITCC

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The unit commitment problem is a complex and essential problem in the power generation field, which is solved to obtain the schedule of a large number of generating units to minimize the operating cost and the fulfillment of consumer load demand. The present work solves the unit commitment problem using quantum-inspired evolutionary algorithms with a novel elitist local search method (QIEA-ELS). The proposed algorithm solves the unit commitment problem efficiently and its applicability is verified on various unit test systems. The constraints are satisfied efficiently to find a feasible solution, the novel elitist search method is used to locally explore the search area around the fittest individual to find a better solution in its vicinity in genotype space represent by qubits. The solution of the unit commitment is carried out considering two small population sizes as suggested in earlier work by other authors using QIEA, though it can be extended using larger population size also. The computational time is also reduced by using the suggested method with a novel elitist local search (ELS) method. The results obtained after applying the proposed algorithm are found to better as compared to other well-known solution techniques.

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Section: Introductionmentioning

confidence: 99%

Quantum Inspired Evolutionary Algorithm with a Novel Elitist Local Search Method for Scheduling of Thermal Units

Singh

Mani

Singh

et al. 2023

IJRITCC

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“…We have, therefore, chosen the harder functions of the CEC LSGO suite [ 17 ] as the testing ground for our proposition. These functions, renowned in the optimization community, embody a myriad of challenges, from multi-modality to shifting landscapes, serving as an ideal crucible to truly assess the mettle of our strategy [ 18 , 19 , 20 ]. the CEC LSGO suite, with its diverse and demanding function set, offers a comprehensive canvas, enabling us to probe the strengths and potential limitations of our approach under varied conditions.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Population on Bio-Inspired Algorithms Using Machine Learning for Global Optimization

Caselli,

Soto,

Crawford

et al. 2023

Biomimetics

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In the optimization field, the ability to efficiently tackle complex and high-dimensional problems remains a persistent challenge. Metaheuristic algorithms, with a particular emphasis on their autonomous variants, are emerging as promising tools to overcome this challenge. The term “autonomous” refers to these variants’ ability to dynamically adjust certain parameters based on their own outcomes, without external intervention. The objective is to leverage the advantages and characteristics of an unsupervised machine learning clustering technique to configure the population parameter with autonomous behavior, and emphasize how we incorporate the characteristics of search space clustering to enhance the intensification and diversification of the metaheuristic. This allows dynamic adjustments based on its own outcomes, whether by increasing or decreasing the population in response to the need for diversification or intensification of solutions. In this manner, it aims to imbue the metaheuristic with features for a broader search of solutions that can yield superior results. This study provides an in-depth examination of autonomous metaheuristic algorithms, including Autonomous Particle Swarm Optimization, Autonomous Cuckoo Search Algorithm, and Autonomous Bat Algorithm. We submit these algorithms to a thorough evaluation against their original counterparts using high-density functions from the well-known CEC LSGO benchmark suite. Quantitative results revealed performance enhancements in the autonomous versions, with Autonomous Particle Swarm Optimization consistently outperforming its peers in achieving optimal minimum values. Autonomous Cuckoo Search Algorithm and Autonomous Bat Algorithm also demonstrated noteworthy advancements over their traditional counterparts. A salient feature of these algorithms is the continuous nature of their population, which significantly bolsters their capability to navigate complex and high-dimensional search spaces. However, like all methodologies, there were challenges in ensuring consistent performance across all test scenarios. The intrinsic adaptability and autonomous decision making embedded within these algorithms herald a new era of optimization tools suited for complex real-world challenges. In sum, this research accentuates the potential of autonomous metaheuristics in the optimization arena, laying the groundwork for their expanded application across diverse challenges and domains. We recommend further explorations and adaptations of these autonomous algorithms to fully harness their potential.

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Evolutionary dynamic grouping based cooperative co-evolution algorithm for large-scale optimization

Yang,

Liu,

Tan

et al. 2024

Appl Intell

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A Novel Memetic Algorithm Based on Multiparent Evolution and Adaptive Local Search for Large-Scale Global Optimization

Cited by 3 publications

References 52 publications

Quantum Inspired Evolutionary Algorithm with a Novel Elitist Local Search Method for Scheduling of Thermal Units

Quantum Inspired Evolutionary Algorithm with a Novel Elitist Local Search Method for Scheduling of Thermal Units

Dynamic Population on Bio-Inspired Algorithms Using Machine Learning for Global Optimization

Evolutionary dynamic grouping based cooperative co-evolution algorithm for large-scale optimization

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