Improved Salp swarm algorithm for solving single-objective continuous optimization problems

Abed-alguni, Bilal H.; Paul, David; Hammad, Rafat

doi:10.1007/s10489-022-03269-x

Cited by 36 publications

(11 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For enhancing quality of the paper, the proposed MBSSO algorithm is compared with different variations of the salp swam optimization algorithm such as Dynamic SSA (DSSA), 21 Binary SSA (bSSA), 22 Improved SSA (iSSA), 23 Memetic SSA (MSSOA), 24 Quantized SSA (QSSA), 25 Enhanced Oppositional based Learning SSA (EOSSA), 26 and Improved SSA (ISSA) 27 . The comparative analyses are conducted in Table 7.…”

Section: Resultsmentioning

confidence: 99%

“…Improved SSA (Improved Salp Swam Optimization Algorithm) 27 : The Highly Disruptive Polynomial mutation (HDPM) is used to generate the improved form of the SSA algorithm for solving single‐objective continuous optimization problems. The improved form enhances the diversity of the initial population that directly impacts the performance of the model.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Mutation boosted salp swarm optimizer meets rough set theory: A novel approach to software defect detection

Sekaran,

Lawrence

2024

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Software defect detection (SDD) is crucial to ensure the reliability of software systems and identify defects in classification. One of the key challenges in defect detection is to select more informative and relevant features from the vast pool of available software metrics. A novel approach is proposed in this paper that leverages mutation boosted salp swarm optimizer (MBSSO) and Rough Set Theory for feature selection (FS) in SDD. It efficiently explores the search space and incorporates a mutation boosting mechanism to overcome local optima. Rough Set Theory provides a formal framework for analyzing feature relevance and dependency, while MBSSO optimizes the search for the optimal feature subset. The proposed approach involves encoding the feature subsets using binary representation and designing a fitness function. The MBSSO algorithm explores the feature space, iteratively improving the feature subset based on the fitness function. The selected feature subset is then used to train a defect detection model using the Kernel Extreme Learning Machine (KELM) algorithm. The experimental validation is performed by Project Repository for Software Engineering (PROMISE) dataset and compared the performance of proposed approach against other FS methods and baselines. The experimental validation demonstrates that the proposed approach achieves superior performance and selected feature subset improves the accuracy and efficiency. This research contributes to the advancement of SDD by providing an effective and efficient FS technique using MBSSO and Rough Set Theory.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Mutation boosted salp swarm optimizer meets rough set theory: A novel approach to software defect detection

Sekaran,

Lawrence

2024

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…A lot of research exists that focuses on single-objective optimization problems [2,3], bi-objective optimization problems [4], or three objectives optimization problems [5,6]. Among them, the problems with more than two and less than three objectives are called MOPs.…”

Section: Motivationmentioning

confidence: 99%

An adaptive reference vector strategy with Q-learning for many-objective workflow scheduling problem in the cloud computing environment

Dong,

Fan,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

Cloud computing environment is widely used in various fields, and the scientific workflow scheduling problem in this environment is a many-objective optimization problem and has attracted much attention. Aiming at meeting the different demands of multiple users, this paper proposes an adaptive many-objective algorithm (AD\_CLIA) based on cascade clustering and reference point incremental learning algorithm (CLIA). First, this paper constructs a workflow scheduling model with four objectives: completion time (makespan), cost load, and average resource utilization (AU). Then, for improving the convergence and diversity of CLIA, a reinforcement learning method for adaptively selecting effective reference vectors is proposed. And at the same time, a double-faced mirror strategy is constructed to deal with the problem of uneven distribution of the optimal solution set. It has shown advantages in both low-dimensional DTLZ test problems and high-dimensional WFG and MaF test problems. Finally, the proposed algorithm is applied to four famous real workflow problems and the results are satisfactory.

show abstract

“…SSA has been successfully utilized in a wide range of optimization problems in different fields, such as machine learning, engineering design, wireless networking, image processing, and power energy. It's an effective single-objective optimization algorithm that was inspired by the navigating and foraging behaviors of salps in their natural habitats [12].…”

Section: B Classroom Resource Allocationmentioning

confidence: 99%

Optimizing Classroom Resource Allocation Using a Modified Salp Swarm Algorithm

Fujii,

Tanaka,

Suzuki

et al. 2024

Preprint

View full text Add to dashboard Cite

Classroom resource allocation is a challenging problem in educational institutions. The use of cloud computing can aid in the optimization of this process by allowing resource providers to allocate resources to users in a timely and efficient manner. In this paper, we propose a modified Salp Swarm Algorithm (SSA) to optimize the allocation of classroom resources. The proposed approach involves generating a population of salps representing potential solutions to the resource allocation problem and updating the positions of the salps iteratively based on a fitness function and mathematical equations. We conducted simulations of the proposed algorithm on various numbers of tasks and sources to evaluate its performance. The results indicate that the modified SSA algorithm can efficiently allocate classroom resources, achieving lower completion time of tasks (makespan) with lower costs. This paper contributes to the literature by introducing an effective approach to solve the classroom resource allocation problem.

show abstract

Improved Salp swarm algorithm for solving single-objective continuous optimization problems

Cited by 36 publications

References 72 publications

Mutation boosted salp swarm optimizer meets rough set theory: A novel approach to software defect detection

Mutation boosted salp swarm optimizer meets rough set theory: A novel approach to software defect detection

An adaptive reference vector strategy with Q-learning for many-objective workflow scheduling problem in the cloud computing environment

Optimizing Classroom Resource Allocation Using a Modified Salp Swarm Algorithm

Contact Info

Product

Resources

About