Location optimization of tower crane as an expensive equipment in the construction projects has an important effect on material transportation costs. Due to the construction site conditions, there are several tower crane location optimization models. Appropriate location of tower cranes for material supply and engineering demands is a combinatorial optimization problem within the tower crane layout problem that is difficult to resolve.  Meta-heuristics are popular and useful techniques to resolve complex optimization problems. In this paper, the performance of the Particle Swarm Optimization (PSO) and four newly developed meta-heuristic algorithms Colliding Bodies Optimization (CBO), Enhanced Colliding Bodies Optimization (ECBO), Vibrating Particles System (VPS), and Enhanced Vibrating Particles System (EVPS) are compared in terms of their effectiveness in resolving a practical Tower Crane Layout (TCL) problem. Results show that ECBO performs better than other three methods in both cases.

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Tower crane is the core construction facility in the high-rise building construction sites. Proper selection and location of construction tower cranes not only can affect the expenses but also it can have impact on the material handling process of building construction. Tower crane selection and layout problem (TCSLP) is a type of construction site layout problem, which is considered as an NP-hard problem. In consequence, researchers have extensively used metaheuristics for their solution. The Sine Cosine Algorithm (SCA) is a newly developed metaheuristic which performs well for TCSLP, however, efficient use of this algorithm requires additional considerations. For this purpose, the present paper studies an upgraded sine cosine algorithm (USCA) that employs a harmony search based operator to improve the exploration and deal with variable constraints simultaneously and uses an archive to save the best solutions. Subsequently, the upgraded sine cosine algorithm is employed to optimize the locations to find the best tower crane layout. Several benchmark functions are studied to evaluate the performance of the USCA. A comparative study indicates that the USCA performs quite well in comparison to other recently developed metaheuristic algorithms.

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KEYWORDS Building energy management system; Multi-objective antlion optimizer; Demand side scheduling; Multi-criteria decision making; CO2 emission; Evidential reasoning.Abstract. Smart-Home Energy Management Systems (SHEMSs) are widely used for energy management in smart buildings. Energy management in smart homes is an arduous task and necessitates e cient scheduling of appliances in buildings. Scheduling of smart appliances is usually enmeshed by various and sometimes contradictory criteria, which should be considered concurrently in the scheduling process. Multi-Criteria Decision Making (MCDM) techniques are able to select the most suitable alternative among copious ones. This paper tailors a comprehensive framework which merges MCDM techniques with Evolutionary Multi-Objective Optimization (EMOO) techniques for selecting the most proper schedule for appliances by creating a trade-o between optimization criteria. A Multi-Objective Ant Lion Optimizer (MOALO) was tailored and tested on a smart home case study to detect all the Pareto solutions. A benchmark instance of the appliance scheduling was solved employing the proposed methodology. Then, Shannon's entropy technique was employed to nd the weights corresponding to the objectives. Finally, the acquired Pareto optimal solutions were ranked utilizing the Evidential Reasoning (ER) method. By inspecting the e ciency of every solution considering multiple criteria such as unsafety, electricity cost, delay, Peak to Average Ratio (PAR), and CO 2 emission, e ectiveness of the proposed approach in enhancing the method for smart appliance scheduling was con rmed.ownership [1]. Therefore, improvement in the energy e ciency of electrical facilities is very in uential for energy-saving in buildings, reducing the loads on electrical grids, and decreasing the carbon footprint. Consequently, electricity conservation in buildings not only results in saving fossil fuels but also prevents capacity expansion in the power sector [2,3]. Many research results are available for supporting the decisions in the management of networks [4,5]. The emergence of smart homes and the Internet has led to an opportunity for automatic operation, scheduling of the appliances, and energy management in residential buildings.