2023
DOI: 10.1007/s42107-023-00572-x
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Building projects with time–cost–quality–environment trade-off optimization using adaptive selection slime mold algorithm

Abstract: Every project manager deals with various challenges, and almost all tasks have backup plans to ensure efficient success. Therefore, it is essential to manage resources, notably in terms of time, cost, quality, and environmental impact, and this needs to be thoroughly shown. As a result, the adaptive selection slime mold algorithm (ASSMA) is proposed for repetitive projects due to multiple concurrent instances. It is made by merging the tournament selection (TS) method and the slime mold algorithm (SMA) model. … Show more

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Cited by 10 publications
(6 citation statements)
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“…4.2.9. Others Moreover, researchers have hybridized an SMA with a sine cosine algorithm [83], marine predators algorithm [85], particle swarm optimization [97], evolutionary algorithm [98], firefly algorithm [99], gray wolf optimization algorithm [100], gradient-based optimizer [101], quadratic approximation [102], tournament selection [103], artificial neural network [104], moth-flame optimization algorithm [105], pattern search algorithm [106], and support vector regression [107]. These hybrid SMA variants indicated their benefits, such as the good balance between exploration and exploitation capabilities, good convergence speed, ability to avoid premature convergence, and reduced computation time.…”
Section: Hybridization With the Artificial Bee Colony (Abc)mentioning
confidence: 99%
“…4.2.9. Others Moreover, researchers have hybridized an SMA with a sine cosine algorithm [83], marine predators algorithm [85], particle swarm optimization [97], evolutionary algorithm [98], firefly algorithm [99], gray wolf optimization algorithm [100], gradient-based optimizer [101], quadratic approximation [102], tournament selection [103], artificial neural network [104], moth-flame optimization algorithm [105], pattern search algorithm [106], and support vector regression [107]. These hybrid SMA variants indicated their benefits, such as the good balance between exploration and exploitation capabilities, good convergence speed, ability to avoid premature convergence, and reduced computation time.…”
Section: Hybridization With the Artificial Bee Colony (Abc)mentioning
confidence: 99%
“…Moreover the researchers hybridize SMA with sine cosine algorithm(SCA) [81] , particle swarm optimization (PSO) [104], evolutionary algorithm(EA) [106], firefly algorithm(FA) [106],grey wolf optimization algorithm(GWOA) [107], marine predators algorithm(MPA) [108], gradient-based optimizer(GO) [109], quadratic approximation(QA) [110], tournament selection(TS) [111], artificial neural network(ANN) [112]],Moth-flame optimization algorithm(MFOA) [113], pattern search algorithm(PSA) [114], support vector regression(SVR) [115] and etc. These hybrid SMA variants have indicated their merits such as the well balance between exploration and exploitation, good convergence speed, avoiding premature convergence, less computation time and so on.…”
Section: Othersmentioning
confidence: 99%
“…Luo Qifang et al [80] proposed a MOEOSMA and assessed it in engineering problems, which indicated its efficiency from comparison. Pham Vu Hong Son et al [111] applied the hybrid model adaptive selection slime mould algorithm (ASSMA) to address the project's multi-objective of time, cost, quality, and environment trade-off problem, which outperformed other multi-objective algorithms through Pareto. Peng C et al [115] proposed a MOSMA and optimized SVR parameters by MOSMA for global convergence.…”
Section: Multi-objective Version Of Smamentioning
confidence: 99%
See 1 more Smart Citation
“…For repeating tasks with several concurrent instances, the adaptive selection slime mould algorithm (ASSMA) is presented by Son and Khoi 24 . Son and Khoi also provides the mutation-crossover slime mold algorithm (MCSMA) for balancing time, cost, quality, and work continuity in a specific building project 25 .…”
Section: Literature Reviewmentioning
confidence: 99%