SPGD: Search Party Gradient Descent Algorithm, a Simple Gradient-Based Parallel Algorithm for Bound-Constrained Optimization

Nature-inspired metaheuristic algorithms remain a strong trend in optimization. Human-inspired optimization algorithms should be more intuitive and relatable. This paper proposes a novel optimization algorithm inspired by a human search party. We hypothesize the behavioral model of a search party se...

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Published in:	Mathematics (Basel) Vol. 10; no. 5; p. 800
Main Authors:	Syed Shahul Hameed, A., Rajagopalan, Narendran
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.03.2022
Subjects:	Algorithms Cooperation Food science Foraging behavior Genetic algorithms gradient-based algorithm group dynamics Heuristic methods human-inspired algorithm Machine learning metaheuristics multi-armed bandits Optimization Optimization algorithms Searching
ISSN:	2227-7390, 2227-7390
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Abstract	Nature-inspired metaheuristic algorithms remain a strong trend in optimization. Human-inspired optimization algorithms should be more intuitive and relatable. This paper proposes a novel optimization algorithm inspired by a human search party. We hypothesize the behavioral model of a search party searching for a treasure. Motivated by the search party’s behavior, we abstract the “Divide, Conquer, Assemble” (DCA) approach. The DCA approach allows us to parallelize the traditional gradient descent algorithm in a strikingly simple manner. Essentially, multiple gradient descent instances with different learning rates are run parallelly, periodically sharing information. We call it the search party gradient descent (SPGD) algorithm. Experiments performed on a diverse set of classical benchmark functions show that our algorithm is good at optimizing. We believe our algorithm’s apparent lack of complexity will equip researchers to solve problems efficiently. We compare the proposed algorithm with SciPy’s optimize library and it is found to be competent with it.
AbstractList	Nature-inspired metaheuristic algorithms remain a strong trend in optimization. Human-inspired optimization algorithms should be more intuitive and relatable. This paper proposes a novel optimization algorithm inspired by a human search party. We hypothesize the behavioral model of a search party searching for a treasure. Motivated by the search party’s behavior, we abstract the “Divide, Conquer, Assemble” (DCA) approach. The DCA approach allows us to parallelize the traditional gradient descent algorithm in a strikingly simple manner. Essentially, multiple gradient descent instances with different learning rates are run parallelly, periodically sharing information. We call it the search party gradient descent (SPGD) algorithm. Experiments performed on a diverse set of classical benchmark functions show that our algorithm is good at optimizing. We believe our algorithm’s apparent lack of complexity will equip researchers to solve problems efficiently. We compare the proposed algorithm with SciPy’s optimize library and it is found to be competent with it.
Author	Syed Shahul Hameed, A. Rajagopalan, Narendran
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SubjectTerms	Algorithms Cooperation Food science Foraging behavior Genetic algorithms gradient-based algorithm group dynamics Heuristic methods human-inspired algorithm Machine learning metaheuristics multi-armed bandits Optimization Optimization algorithms Searching
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Title	SPGD: Search Party Gradient Descent Algorithm, a Simple Gradient-Based Parallel Algorithm for Bound-Constrained Optimization
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