The analysis of the relationship between time and cost is a crucial aspect of construction project management. Various optimization techniques have been developed to solve time-cost trade-off ...problems. A hybrid multi-verse optimizer model (hDMVO) is introduced in this study, which combines the multi-verse optimizer (MVO) and the sine cosine algorithm (SCA) to address the discrete time-cost trade-off problem (DTCTP). The algorithm's optimality is evaluated by using 23 well-known benchmark test functions. The results demonstrate that hDMVO is competitive with MVO, SCA, the dragonfly algorithm and ant lion optimization. The performance of hDMVO is evaluated using four benchmark test problems of DTCTP, including two medium-scale instances (63 activities) and two large-scale instances (630 activities). The results indicate that hDMVO can provide superior solutions in the time-cost optimization of large-scale and complex projects compared to previous algorithms.
The present study focuses on the problem of vehicle routing with limited capacity, with the objective of minimizing the transportation distance required to serve h clients with predetermined ...locations and needs. The aim is to create k trips that cover the shortest possible distance. To achieve this goal, a hybrid whale optimization algorithm (hGWOA) is proposed, which combines the whale optimization algorithm (WOA) with the grey wolf optimizer (GWO). The proposed hybrid model is comprised of two main steps. First step, the GWO's hunting mechanism is integrated transitioning to the utilization phase of WOA, and a newly devised state is introduced that is linked to GWO. In the second step, a novel technique is incorporated into the exploration mission phase to enhance the resolve after per iteration. The algorithm's performance is assessed and compared with other modern algorithms, including the GWO, WOA, ant lion optimizer (ALO), and dragonfly algorithm (DA) using 23 benchmark test functions and CEC2017 benchmark test function. The results indicate that the hybrid hGWOA method outperforms other algorithms in terms of delivery distance optimization for scenarios involving scale and complexity. These findings are corroborated through case studies related to cement delivery and a real-world scenario in Viet Nam.
The sine cosine algorithm (SCA) is widely recognized for its efficacy in solving optimization problems, although it encounters challenges in striking a balance between exploration and exploitation. ...To improve these limitations, a novel model, termed the novel sine cosine algorithm (nSCA), is introduced. In this advanced model, the roulette wheel selection (RWS) mechanism and opposition-based learning (OBL) techniques are integrated to augment its global optimization capabilities. A meticulous evaluation of nSCA performance has been carried out in comparison with state-of-the-art optimization algorithms, including multi-verse optimizer (MVO), salp swarm algorithm (SSA), moth-flame optimization (MFO), grasshopper optimization algorithm (GOA), and whale optimization algorithm (WOA), in addition to the original SCA. This comparative analysis was conducted across a wide array of 23 classical test functions and 29 CEC2017 benchmark functions, thereby facilitating a comprehensive assessment. Further validation of nSCA utility has been achieved through its deployment in five distinct engineering optimization case studies. Its effectiveness and relevance in addressing real-world optimization issues have thus been emphasized. Across all conducted tests and practical applications, nSCA was found to outperform its competitors consistently, furnishing more effective solutions to both theoretical and applied optimization problems.
Meta-heuristic algorithms distinguish themselves from conventional optimization methods owing to their intrinsic adaptability and straightforward implementation. Among them, the sine cosine algorithm ...(SCA) is lauded for its ability to transition seamlessly between exploration and exploitation phases throughout the optimization process. However, there exists potential for enhancing the balance that SCA maintains between exploration and exploitation. To augment the proficiency in global optimization of SCA, an innovative strategy-nSCA-that integrates the roulette wheel selection (RWS) with opposition-based learning was formulated. The robustness of nSCA was rigorously evaluated against leading-edge methods such as the genetic algorithm (GA), particle swarm optimization, moth-flame optimization, ant lion optimization, and multi-verse optimizer, as well as the foundational SCA. This evaluation included benchmarks set by both CEC 2019 and CEC 2021 test functions. Additionally, the performance of nSCA was confirmed through numerous practical optimization problems, emphasizing its effectiveness in applied settings. In all evaluations, nSCA consistently showcased superior performance compared to its evolutionary algorithm counterparts, delivering top-tier solutions for both benchmark functions and real-world optimization challenges. Given this compelling evidence, one can posit that nSCA serves as a strong candidate for addressing intricate optimization challenges found in real-world contexts, regardless of whether they are of a discrete or continuous nature.
The objective of this study is to utilize the geometric mean optimizer (GMO) for mass optimization of structural trusses. By harnessing the GMO’s mutation mechanism rooted in a Gaussian framework, ...the model effectively addresses the discrete nature of truss structure optimization. Through a comprehensive evaluation involving four distinct problem scenarios including 10, 15, 25, and 52-bar truss structures with both discrete and continuous variables, the effectiveness of the GMO technique is thoroughly demonstrated. The optimization findings underscore that the GMO consistently generates improved designs in comparison to conventional population-based techniques. Furthermore, the GMO model demonstrates remarkable computational efficiency in specific cases. This research emphasizes the potential of the GMO-based approach as a potent tool in the domain of truss structure optimization. It holds the capability to revolutionize the manner in which engineers approach the intricate balance between structural integrity, minimal weight, and cost-effectiveness.
Purpose For successful management of construction projects, a precise analysis of the balance between time and cost is imperative to attain the most effective results. The aim of this study is to ...present an innovative approach tailored to tackle the challenges posed by time-cost trade-off (TCTO) problems. This objective is achieved through the integration of the multi-verse optimizer (MVO) with opposition-based learning (OBL), thereby introducing a groundbreaking methodology in the field. Design/methodology/approach The paper aims to develop a new hybrid meta-heuristic algorithm. This is achieved by integrating the MVO with OBL, thereby forming the iMVO algorithm. The integration enhances the optimization capabilities of the algorithm, notably in terms of exploration and exploitation. Consequently, this results in expedited convergence and yields more accurate solutions. The efficacy of the iMVO algorithm will be evaluated through its application to four different TCTO problems. These problems vary in scale – small, medium and large – and include real-life case studies that possess complex relationships. Findings The efficacy of the proposed methodology is evaluated by examining TCTO problems, encompassing 18, 29, 69 and 290 activities, respectively. Results indicate that the iMVO provides competitive solutions for TCTO problems in construction projects. It is observed that the algorithm surpasses previous algorithms in terms of both mean deviation percentage (MD) and average running time (ART). Originality/value This research represents a significant advancement in the field of meta-heuristic algorithms, particularly in their application to managing TCTO in construction projects. It is noteworthy for being among the few studies that integrate the MVO with OBL for the management of TCTO in construction projects characterized by complex relationships.
The management of construction projects has long emphasized the delicate balance between time and cost, as these factors play a critical role in achieving optimal project outcomes. To address this ...challenge, stochastic optimization algorithms have emerged as valuable tools. One such algorithm, moth-flame optimization (MFO), leverages its capacity to navigate complex and unknown search spaces. When combined with the tournament selection (TS) method, which is designed to maintain diversity and control the convergence rate by providing equal opportunities for all individuals to be selected, it demonstrates remarkable potential and competitiveness in solving challenging problems with constraints. This research introduces an enhanced version of the MFO model, called TMFO, as an innovative approach to address time–cost trade-off (TCTO) problems in construction project management. To assess its performance, three benchmark test problems are employed, including two case studies involving 7 activities and one case study with 18 activities. The results reveal that TMFO outperforms other optimization algorithms when applied to TCTOs in small-scale projects. These findings underscore the effectiveness and relevance of the TMFO algorithm within the domain of construction project management.
The global construction industry plays a pivotal role, yet its unique characteristics pose distinctive challenges. Each construction project, marked by its individuality, substantial value, intricate ...scale, and constrained adaptability, confronts crucial limitations concerning time and cost. Despite contributing significantly to environmental concerns throughout construction activities and infrastructure operations, environmental considerations remain insufficiently addressed by project managers. This research introduces an improved rendition of the muti-objective grasshopper optimization algorithm (MOGOA), termed eMOGOA, as a novel methodology to tackle time, cost, and carbon dioxide emission trade-off problems (TCCP) in construction project management. To gauge its efficacy, a case study involving 29 activities is employed. eMOGOA amalgamates MOGOA, tournament selection (TS), and opposition-based learning (OBL) techniques to enhance the performance of the original MOGOA. The outcomes demonstrate that eMOGOA surpasses other optimization algorithms, such as MODA, MOSMA, MOALO and MOGOA when applied to TCCP. These findings underscore the efficiency and relevance of the eMOGOA algorithm within the realm of construction project management.
The construction industry holds a central role globally, marked by its unique attributes that lead to distinct challenges. Given projects in this sector are often tailored to specific needs, operate ...on a grand scale, showcase complex designs, and have limited adaptability, the industry frequently confronts the dual challenges of time and cost. In construction project management, a fundamental task is to harmonize these time and cost considerations effectively. The Multi-Verse Optimizer (MVO) algorithm has been identified as a potential tool to navigate the intricate search spaces typical of construction endeavors. However, MVO is not without its challenges, specifically its slower convergence rate and propensity for entrapment in local optima. In response to these challenges, this research introduces the Adaptive Multi-Verse Optimizer (aMVO) model. By integrating the Modified Adaptive Weight Approach (MAWA) with the MVO algorithm, the aMVO aims to bolster optimization efficiency. Centering on the complex domain of time-cost optimization problems (TCP), this study embarks on an in-depth evaluation of the aMVO against benchmarks covering scenarios with 18, 90, and 180 activities. The findings compellingly underscore the superiority of aMVO in navigating the complexities of TCP, particularly when contrasted with methods such as GA, MFO, SCA, DA, and ALO. This firmly positions aMVO as an indispensable tool in the domain of construction project management.
