BSMA: A novel metaheuristic algorithm for multi-dimensional knapsack problems: Method and comprehensive analysis

Abdel‐Basset, Mohamed; Mohamed, Reda; Sallam, Karam M.; Chakrabortty, Ripon K.; Ryan, Michael J.

doi:10.1016/j.cie.2021.107469

Cited by 25 publications

(5 citation statements)

References 29 publications

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“…The efficiency of bBSSMA in FS underwent an extensive comparative analysis against seven notable optimization techniques-BGWO [96], BGSA [14], BPSO [75], Binary Ant Lion Optimization (BALO) [76], BBA [97], Binary Salp Swarm Algorithm (BSSA) [98], and BSMA [99]. This evaluation spanned across a diverse set of 12 UCI datasets [95] and encompassed the examination of multiple performance metrics, including average fitness value, the average number of features selected, average runtime, and average accuracy rate.…”

Section: Experimental Results and Analysis Of Fsmentioning

confidence: 99%

Enhanced slime mould algorithm with backtracking search algorithm: global optimization and feature selection

Wang,

Chen,

Zou

et al. 2024

Preprint

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The Slime Mould Algorithm (SMA), renowned for its swarm-based approach, encounters challenges, particularly in maintaining a balance between exploration and exploitation, leading to a trade-off that impacts its optimization performance. The simple structure and limited hyperparameters of SMA contribute to difficulties in effectively navigating the exploration-exploitation trade-off, with a drawback being its poor ability for exploration. To address these challenges and enhance SMA, this paper introduces BSSMA, an improved variant that incorporates the Backtracking Search Algorithm (BSA). The introduction of the \(phaseratio\) parameter aims to synergize BSA and SMA, capitalizing on the strengths of both algorithms while mitigating their individual drawbacks, including SMA's poor exploration ability. BSA facilitates a thorough exploration, dispersing search agents widely across the solution space, ensuring significant diversity. These search agents then transition to SMA to further refine the search for optimal solutions while addressing SMA's exploration limitations. Evaluating the performance of BSSMA involves comparisons with 12 other meta-heuristic algorithms (MAs) and 10 advanced MAs using the CEC2017 benchmark functions. Experimental results showcase that the enhanced BSSMA outperforms SMA in terms of convergence speed and accuracy, specifically addressing the challenges associated with balancing exploration and exploitation trade-offs, including SMA's poor exploration ability. Additionally, to demonstrate BSSMA's effectiveness in practical engineering applications, a binary version (bBSSMA) is developed for feature selection (FS) using a V-shaped transfer function. Comparative experiments with seven other binary MA variants reveal that bBSSMA selects fewer features, attains higher classification accuracy, and demands less computational time. These results affirm the effectiveness of bBSSMA for practical feature selection applications.

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Section: Experimental Results and Analysis Of Fsmentioning

confidence: 99%

Enhanced slime mould algorithm with backtracking search algorithm: global optimization and feature selection

Wang,

Chen,

Zou

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…These datasets possess a substantial number of features, ranging from 22,277 to 54,675. To comprehensively display bIRIME's performance, it was compared against bMFO [95], bGWO [91], bSMA [96], bALO [97], BBA [98], BSSA [99], bWOA [100], and bHHO [101]. The dimension size depends on the dataset's dimensionality, and the population size is set to 20.…”

Section: Simulation Experimentsmentioning

confidence: 99%

IRIME: A Novel Approach to Mitigating Exploitation-Exploration Imbalance in Rime Optimization Algorithm for Feature Selection

Huang,

Chen,

Heidari

et al. 2024

Preprint

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Rime optimization algorithm (RIME) is an emerging metaheuristic algorithm. However, RIME encounters issues such as an imbalance between exploitation and exploration, susceptibility to local optima, and low convergence accuracy when handling problems. To address these drawbacks, this paper introduces a variant of RIME called IRIME. IRIME integrates the soft besiege (SB) and composite mutation strategy and restart strategy (CMS-RS), aiming to balance exploitation and exploration in RIME, enhance population diversity, improve convergence accuracy, and endow RIME with the capability to escape local optima. To comprehensively validate IRIME's performance, IEEE CEC 2017 benchmark tests were conducted, comparing it against 13 conventional algorithms and 11 advanced algorithms, including excellent algorithms in the CEC competition such as JADE. The results indicate that the performance of IRIME is the best. To validate IRIME's practical applicability, the paper proposes a binary version, bIRIME, applied to feature selection problems. bIRIMR performs well on 12 low-dimensional datasets and 24 high-dimensional datasets. It outperforms other advanced algorithms in terms of the number of feature subsets and classification accuracy. In conclusion, bIRIME performs notably well in feature selection, particularly in high-dimensional datasets.

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“…( 2020b ) 79 Binary Slime Mould Algorithm (BSMA) Abdel-Basset et al. ( 2021 ) 80 Binary Whale Optimization Algorithm (bWOA) Reddy K et al. ( 2019 ) 81 Biogeography-Based Optimization(BBO) Simon ( 2008 ) 82 Biology Migration Algorithm (BMA) Zhang et al.…”

Section: Metaheuristicsmentioning

confidence: 99%

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

2023

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As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called ‘novel’ if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on ‘novel ideas’, so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.

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BSMA: A novel metaheuristic algorithm for multi-dimensional knapsack problems: Method and comprehensive analysis

Cited by 25 publications

References 29 publications

Enhanced slime mould algorithm with backtracking search algorithm: global optimization and feature selection

Enhanced slime mould algorithm with backtracking search algorithm: global optimization and feature selection

IRIME: A Novel Approach to Mitigating Exploitation-Exploration Imbalance in Rime Optimization Algorithm for Feature Selection

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

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