Post-occupancy Evaluation Parameters in Multi-objective Optimization–Based Design Process

This study was a post-occupancy evaluation (POE) to examine the effectiveness of three specific design innovations in a mental healthcare facility. In addition to collecting data about the impact of these specific designs, the study provides a model for the broader implementation of POE approaches in the mental healthcare context. POEs in general healthcare settings have been shown to lead to better work environments and better outcomes for patients. Despite growing evidence of the value provided by POE studies, the industry has been somewhat slow to adopt their regular use, in part due to unfamiliarity with the POE process. This is particularly true in mental healthcare contexts, where POE studies remain virtually nonexistent. In-depth interviews and a widely distributed, anonymous survey were used to collect hospital staff perceptions and feedback regarding the impact of specific design features. The hospital staff were quite enthusiastic about two of the design innovations studied here (a new wayfinding strategy and the use of vibrant colors in specific areas of the facility). The third innovation, open-style communication centers, elicited more mixed evaluations. The results include extensive hypothesis testing about the effects of each innovation as well as narrative discussions of their pros and cons. The study generated new knowledge about three specific mental healthcare design innovations and provides a model for the practical implementation of a POE approach in mental healthcare contexts. The results are particularly relevant for designers who are considering innovative strategies in future mental healthcare facilities.

In this paper, the problems of fuel cost and emission reduction based on multi-objective optimization are studied and solved. Various multi-objective optimization algorithms, such as the multi-objective ant lion optimization (MOALO), the multi-objective multiverse optimization (MOMVO), the multi-objective particle swarm optimization (MOPSO), the multi-objective thermal exchange optimization (MOTEO), and the multi-objective grasshopper optimization algorithm (MOGOA), are used to reduce the cost and emissions of generation fuel. With equality and inequality restrictions, the suggested work is carried out in an IEEE 30 bus system. To determine the lowest system-wide minimization of generating cost and emission, the results of several optimization strategies are compared. In terms of lowering fuel costs and emissions, the MOGOA performs better than any other multi-objective algorithms.

A series of post occupancy evaluation (POE) studies have been carried out as part of the research for a M. Phil (Architecture) degree at the University of Sydney. The aim of the studies was to isolate causes of dissatisfaction within the large open plan office environment. It was hypothesized that the adaptation level would be a significant influence on peoples' perceptions of their visual environment. The outcomes from laboratory based research had indicated that situations where the mean adaptation levels were greater than 30 to 40 cd/m2 would be judged bright and well lit. The outcomes from the POE studies disagreed with these findings and mean adaptation levels in excess of 100 cd/m2 were associated with conditions judged to be dim, gloomy and inadequately lit. There seems a possibility that daylight glare from windows destabilized the visual function and set adaptation levels that caused well illuminated spaces to appear inadequately lit. Implications for lighting design are raised and the need for ongoing research is identified.

With the complexity of mechanical manufacturing system design increasing, the traditional single-objective optimization method can no longer meet the needs of multi-objective optimization, such as lightweight, high efficiency, low energy consumption and long life. Multi-objective optimization algorithm is widely used in mechanical design and manufacturing process planning because of its good convergence and diversity. Taking robot motion planning as an example, this paper analyzes the application methods of multi-objective optimization algorithm in mechanical structure design in detail, including problem description, multi-objective optimization model construction, algorithm application and performance analysis. Through the application of Genetic Algorithm (GA), this paper shows how the multi-objective optimization algorithm can balance multiple objectives such as path length, energy consumption and obstacle avoidance effect, and realize more intelligent and efficient solutions in robot motion planning. Performance analysis shows that GA is superior to single-objective optimization and random search algorithm in optimization effect, computational efficiency and stability. In addition, this paper also discusses the influence of the change of target weight and algorithm parameters on the optimization results, which provides valuable reference for future algorithm optimization and practical application. Multi-objective optimization algorithm shows obvious advantages in mechanical manufacturing system, which provides new ideas and methods for realizing more efficient, more environmentally friendly and more intelligent mechanical manufacturing design.

Investigation of the feasibility of POE methodology for a modern commercial office building

In this paper, we propose a Multi-objective Five-element Cycle Optimization algorithm based on Multi-strategy fusion (MOFECO-MS) to address the Bi-objective Traveling Thief Problem (BITTP), an extension of the Traveling Thief Problem that incorporates two conflicting objectives. The novelty of our approach lies in a unique individual selection strategy coupled with an innovative element update mechanism rooted in the Five-element Cycle Model. To balance global exploration and local exploitation, the algorithm categorizes the population into distinct groups and applies crossover operations both within and between these groups, while also employing a mutation operator for local searches on the best individuals. This coordinated approach optimizes parameter settings and enhances the search capabilities of the algorithm. Extensive experiments were conducted on nine BITTP instances, comparing MOFECO-MS against eight state-of-the-art multi-objective optimization algorithms. The results show that MOFECO-MS excels in both Hypervolume (HV) and Spread (SP) indicators, while also maintaining a high level of Pure Diversity (PD). Overall, MOFECO-MS outperformed the other algorithms in most instances, demonstrating its superiority and robustness in solving complex multi-objective optimization problems.

The University of Mosul has recently witnessed a construction of several departments, administrative buildings and lecture halls which may suffer after occupation some environmental or functional problems that require deep studies and researches to diagnose those problems, Hence comes the importance of post occupancy evaluation studies of those buildings as an integral part of the planning , design and construction processes to explore what is positive and negative in the design process , and get benefit from such researches to adopt the positives to enrich the design process and stand on the negatives and their causes to find an appropriate solutions and avoid repeating them in the design of future buildings. The problems stemming from the indoor environmental quality in the lecture halls comes at the front of these negatives and in particular the daylight and the use of it to enriches the indoor environment and it is supportive impact on students and lecturers performance and psychological health on one hand and the rational consumption of energy expended on artificial lighting on the other. This study focuses on the post occupancy evaluation of daylighting in the lecture halls at the new buildings of the University of Mosul, and the college of Electronic Engineering has been chosen as a case study because the adoption of one typical design to all of its four departments and replicated in other locations. The study reveals a deficiency of utilization of daylight as a design factor to provide visual comfort for the occupants of these buildings through the cognitive and realistic measurements adopted for this study.

A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems

ABSTRACTTo address multiobjective, multi constraint and time-consuming structural optimization problems in a vehicle axle system, a multiobjective cooperative optimization model of a vehicle axle structure is established. In light of the difficulty in the nondominated sorting of the NSGA-II algorithm caused by inconsistent effects of the uniformity objective function and physical objective function, this paper combines a multiobjective genetic algorithm with cooperative optimization and presents a strategy for handling the optimization of a vehicle axle structure. The uniformity objective function of the sub discipline is transformed to its self-constraint. Taking the multiobjective optimization of a vehicle axle system as an example, a multiobjective cooperative optimization design for the system is carried out in ISIGHT. The results show that the multiobjective cooperative optimization strategy can simplify the complexity of optimization problems and that the multiobjective cooperative optimization method based on an approximate model is favorable for accuracy and efficiency, thereby providing a theoretical basis for the optimization of similar complex structures in practical engineering.

Battle Royale Optimizer (BRO) is a recently proposed optimization algorithm that has added a new category named game-based optimization algorithms to the existing categorization of optimization algorithms. Both continuous and binary versions of this algorithm have already been proposed. Generally, optimization problems can be divided into single-objective and multi-objective problems. Although BRO has successfully solved single-objective optimization problems, no multi-objective version has been proposed for it yet. This gap motivated us to design and implement the multi-objective version of BRO (MOBRO). Although there are some multi-objective optimization algorithms in the literature, according to the no-free-lunch theorem, no optimization algorithm can efficiently solve all optimization problems. We applied the proposed algorithm to four benchmark datasets: CEC 2009, CEC 2018, ZDT, and DTLZ. We measured the performance of MOBRO based on three aspects: convergence, spread, and distribution, using three performance criteria: inverted generational distance, maximum spread, and spacing. We also compared its obtained results with those of three state-of-the-art optimization algorithms: the multi-objective Gray Wolf optimization algorithm (MOGWO), the multi-objective particle swarm optimization algorithm (MOPSO), the multi-objective artificial vulture's optimization algorithm (MOAVAO), the optimization algorithm for multi-objective problems (MAOA), and the multi-objective non-dominated sorting genetic algorithm III (NSGA-III). The obtained results approve that MOBRO outperforms the existing optimization algorithms in most of the benchmark suites and operates competitively with them in the others.

In order to solve the problem of the multi-objective spectrum allocation on the joint optimization of maximal network utility and fairness of users in cognitive radio network, based on quantum bee colony theory and membrane computing, a novel multi-objective discrete combinatorial optimization algorithm, named membrane-inspired quantum bee optimization, is proposed. The global optimal solution of single objective can be searched in the elementary membranes, and Pareto front solutions which take account of network utility and fairness, can be obtained from skin membrane with the proposed method. The multi-objective optimization algorithm, which can solve both single objective and multi-objective optimization problems at the same time, is designed by the communication rules between membranes, the cooperative evolution of foraging behavior based on quantum state, and non-dominated sorting. Compared with classical color-sensitive graph coloring algorithm, genetic algorithm, quantum genetic algorithm, and particle swarm optimization under different objective functions, the proposed spectrum allocation method can search the global optimal solution of single objective as shown by the simulation results, and it is superior to classical spectrum allocation algorithms and existing intelligence spectrum allocation methods. The optimal Pareto front solutions of multi-objective spectrum allocation are also obtained.

The integration and application of a new multi-objective tabu search optimization algorithm for Fluid Structure Interaction (FSI) problems are presented. The aim is to enhance the computational design process for real world applications and to achieve higher performance of the whole system for the four considered objectives. The described system combines the optimizer with a well established FSI solver which is based on the fully implicit, monolithic formuFlation of the problem in the Arbitrary Lagrangian-Eulerian FEM approach. The proposed solver resolves the proposed uid-structure interaction benchmark which describes the self-induced elastic deformation of a beam attached to a cylinder in laminar channel ow. The optimized ow characteristics of the aforementioned geometrical arrangement illustrate the performance of the system in two dimensions. Special emphasis is given to the analysis of the simulation package, which is of high accuracy and is the core of application. The design process identifies the best combination of ow features for optimal system behavior and the most important objectives. In addition, the presented methodology has the potential to run in parallel, which will significantly speed-up the elapsed time. Finite Element Method (FEM), Fluid-Structure Interaction (FSI), Multi-Ojective Tabu search (MOTS2). Copyright © 2013 Tech Science Press.

This paper discusses the importance of critical success factors (CSFs) for post occupancy evaluation (POE) of hospital building performance. Recent failures of hospital buildings in delivering the expected service have raised the need for POE of their actual performance. However, POE of hospital building performance is a difficult undertaking and requires a vast amount of resources. Management, competencies, culture and awareness have been recognised as the factors that impede the success of POE projects. This demands the identification of the critical success factors (CSFs) that will enable POE projects to be undertaken without the success-impeding factors aforementioned, hence leading to successful POE projects. Despite the numerous studies on POE, the CSFs for POE have not been investigated. This leads to a knowledge gap of what are the actual CSFs that need to be considered in ensuring the success rate of hospital POE projects. By reviewing various related literatures, this paper attempts to generally look at the possibility of conducting a study on CSFs to ensure the success of hospital POE projects. This paper will eventually review the need for CSFs for POE of hospital building performance.

Due to the adversarial limitations between the output solutions in the multi-objective optimization stage, the energy efficiency optimization of large pumping stations has the disadvantage of difficult coordination between power consumption and efficiency. Therefore, a multi-objective Bayesian hyperparameter optimization algorithm design for energy efficiency optimization of large pumping stations has been carried out. A multi-objective optimization model for energy efficiency of large pumping stations was constructed by combining the input power of the main motor, transmission energy loss, transformer loss, and energy consumption of auxiliary equipment, with efficiency as the constraint. We extract sequence features that affect pump water demand, and use feature labels as hyperparameters for the multi-objective optimization model of energy efficiency in large pumping stations. We transform the multi-objective optimization problem into a hyperparameter optimization problem, avoiding adversarial output solutions. We iterate the query points collected by EI, so the output model reaches a convergent optimal solution. The test results show that the designed algorithm can improve the pumping efficiency of the pump station while controlling the power consumption of the pump station, achieving collaborative optimization of efficiency.

This paper deals with the multi-objective optimization of tire reinforcement structures such as the tread belt and the carcass path. The multi-objective functions are defined in terms of the discrete-type design variables and approximated by artificial neutral network, and the sensitivity analyses of these functions are replaced with the iterative genetic evolution. The multi-objective optimization algorithm introduced in this paper is not only highly CPU-time-efficient but it can also be applicable to other multi-objective optimization problems in which the objective function, the design variables and the constraints are not continuous but discrete. Through the illustrative numerical experiments, the fiber-reinforced tire belt structure is optimally tailored. The proposed multi-objective optimization algorithm is not limited to the tire reinforcement structure, but it can be applicable to the generalized multi-objective structural optimization problems in various engineering applications.