Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm

Agrawal, Prachi; Ganesh, Talari; Mohamed, Ali Wagdy

doi:10.1007/s40747-021-00351-8

Cited by 35 publications

(16 citation statements)

References 36 publications

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“…Other recent work by [138] addresses the knapsack problem by utilizing a binary gaining sharing knowledgebased optimization algorithm. The Gaining Sharing Knowledge-based (GSK) optimization algorithm addresses binary optimization problems based on the concept of acquisition and sharing of knowledge of humans during their lifetime.…”

Section: Trajectory-based Algorithms (Tbas)mentioning

confidence: 99%

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

Omolara

Abiodun

Alawida

et al. 2021

Neural Comput & Applic

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Specialized data preparation techniques, ranging from data cleaning, outlier detection, missing value imputation, feature selection (FS), amongst others, are procedures required to get the most out of data and, consequently, get the optimal performance of predictive models for classification tasks. FS is a vital and indispensable technique that enables the model to perform faster, eliminate noisy data, remove redundancy, reduce overfitting, improve precision and increase generalization on testing data. While conventional FS techniques have been leveraged for classification tasks in the past few decades, they fail to optimally reduce the high dimensionality of the feature space of texts, thus breeding inefficient predictive models. Emerging technologies such as the metaheuristics and hyper-heuristics optimization methods provide a new paradigm for FS due to their efficiency in improving the accuracy of classification, computational demands, storage, as well as functioning seamlessly in solving complex optimization problems with less time. However, little details are known on best practices for case-to-case usage of emerging FS methods. The literature continues to be engulfed with clear and unclear findings in leveraging effective methods, which, if not performed accurately, alters precision, real-world-use feasibility, and the predictive model's overall performance. This paper reviews the present state of FS with respect to metaheuristics and hyper-heuristic methods. Through a systematic literature review of over 200 articles, we set out the most recent findings and trends to enlighten analysts, practitioners and researchers in the field of data analytics seeking clarity in understanding and implementing effective FS optimization methods for improved text classification tasks.

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Section: Trajectory-based Algorithms (Tbas)mentioning

confidence: 99%

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

Omolara

Abiodun

Alawida

et al. 2021

Neural Comput & Applic

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“…To verify the performance of the two algorithms, i.e., DEHLO1 and DEHLO2, the proposed DEHLOs as well as other six binary-coding optimization algorithms, i.e., Improved Adaptive Human Learning Optimization (IAHLO) [ 37 ], Simple Human Learning Optimization (SHLO) [ 34 ], Modified Binary Differential Evolution (MBDE) [ 47 ], Novel Binary Differential Evolution (NBDE) [ 53 ], Improved Binary Particle Swarm Optimization (IBPSO) [ 54 ], and Novel Binary Gaining Sharing Knowledge-based optimization (NBGSK) [ 17 ], were applied to solve multidimensional knapsack problems [ 55 ]. The parameters pr, pi, CR, F, and b adopt the default values of HLO and MBDE, and a set of fair parameters, i.e., Cn and K of DEHLO1 and NM and NH of DEHLO2, is chosen for DEHLO1 and DEHLO2 by trial and error in this paper, that is, Cn = 100, K = 5%, NM = 100, and NH = 50.…”

Section: Resultsmentioning

confidence: 99%

“…Metaheuristics are general frameworks to build heuristics for combinatorial and global optimization problems [ 3 ]. The application of natural or biology-inspired metaheuristic optimizations, such as Genetic Algorithm [ 4 ], Particle Swarm Optimization [ 5 ], Harmony Search [ 6 ], Differential Evolution (DE) [ 7 – 10 ], Artificial Bee Colony [ 11 ], Fruit Fly Optimization [ 12 ], Distributed Grey Wolf Optimizer (DGWO) [ 13 ], Moth Search Algorithm (MSA) [ 14 ], Slime Mould Algorithm (SMA) [ 15 ], Gaining Sharing Knowledge-Based Optimization [ 16 , 17 ], Cuckoo Search with Exploratory (ECS) [ 18 ], Discrete Jaya with Refraction Learning and Three Mutation (DJRL3M) [ 19 ], and Monarch Butterfly Optimization (MBO) [ 20 ], Hunger Games Search (HGS) [ 21 ], Runge Kutta Method (RUN) [ 22 ], and Harris Hawks Optimization (HHO) [ 23 ], has been very successful to solve the complex optimization problems, such as feature selection [ 24 – 28 ], image segmentation [ 29 ], controller designation [ 30 ], flow-shop scheduling problem [ 31 , 32 ], and the node placement of wireless sensor networks [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Differential Human Learning Optimization Algorithm

Zhang

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Human Learning Optimization (HLO) is an efficient metaheuristic algorithm in which three learning operators, i.e., the random learning operator, the individual learning operator, and the social learning operator, are developed to search for optima by mimicking the learning behaviors of humans. In fact, people not only learn from global optimization but also learn from the best solution of other individuals in the real life, and the operators of Differential Evolution are updated based on the optima of other individuals. Inspired by these facts, this paper proposes two novel differential human learning optimization algorithms (DEHLOs), into which the Differential Evolution strategy is introduced to enhance the optimization ability of the algorithm. And the two optimization algorithms, based on improving the HLO from individual and population, are named DEHLO1 and DEHLO2, respectively. The multidimensional knapsack problems are adopted as benchmark problems to validate the performance of DEHLOs, and the results are compared with the standard HLO and Modified Binary Differential Evolution (MBDE) as well as other state-of-the-art metaheuristics. The experimental results demonstrate that the developed DEHLOs significantly outperform other algorithms and the DEHLO2 achieves the best overall performance on various problems.

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“…They observed that GSK algorithm gives significantly better results as compared to other metaheuristic algorithms in terms of accuracy, convergence, and can find the optimal solutions. Moreover, Agrawal et al [37][38][39][40] proposed binary versions of the GSK algorithm and applied it to the real-world problems such as feature selection problem, knapsack problem.…”

Section: Figure 1: Classification Of Metaheuristic Algorithmsmentioning

confidence: 99%

Gaining-Sharing Knowledge Based Algorithm for Solving Stochastic Programming Problems

Agrawal¹,

Alnowibet²,

Mohamed³

2022

Computers, Materials &Amp; Continua

Self Cite

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This paper presents a novel application of metaheuristic algorithms for solving stochastic programming problems using a recently developed gaining sharing knowledge based optimization (GSK) algorithm. The algorithm is based on human behavior in which people gain and share their knowledge with others. Different types of stochastic fractional programming problems are considered in this study. The augmented Lagrangian method (ALM) is used to handle these constrained optimization problems by converting them into unconstrained optimization problems. Three examples from the literature are considered and transformed into their deterministic form using the chance-constrained technique. The transformed problems are solved using GSK algorithm and the results are compared with eight other state-of-the-art metaheuristic algorithms. The obtained results are also compared with the optimal global solution and the results quoted in the literature. To investigate the performance of the GSK algorithm on a real-world problem, a solid stochastic fixed charge transportation problem is examined, in which the parameters of the problem are considered as random variables. The obtained results show that the GSK algorithm outperforms other algorithms in terms of convergence, robustness, computational time, and quality of obtained solutions.

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Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm

Cited by 35 publications

References 36 publications

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

Differential Human Learning Optimization Algorithm

Gaining-Sharing Knowledge Based Algorithm for Solving Stochastic Programming Problems

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