Comparison of multi-objective optimization methodologies for engineering applications

Chiandussi, Giorgio; Codegone, Marco; Ferrero, Sergio W.; Varesio, F. E.

doi:10.1016/j.camwa.2011.11.057

Cited by 368 publications

(178 citation statements)

References 10 publications

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“…For the purpose of balancing the magnitude and unit differences, each output variable F was normalized as (Chiandussi et al, ):

F_{n} = \frac{F ((),,, p_{1}, p_{2}, p_{3}) - F {(p_{1}, p_{2}, p_{3})}_{\min}}{F {(p_{1}, p_{2}, p_{3})}_{\max} - F {(p_{1}, p_{2}, p_{3})}_{\min}}

where p 1 , p 2 , and p 3 represents the input variable combination (air temperature, air velocity, and precooling time) and F n is the normalized value of the function. Different combinations of air velocity (0.58, 5, 10 m s −1 ), air temperature (0, −15, and −30°C) and precooling time (2, 4, and 6 hr) were considered.…”

Section: Methodsmentioning

confidence: 99%

“…Different combinations of air velocity (0.58, 5, 10 m s −1 ), air temperature (0, −15, and −30°C) and precooling time (2, 4, and 6 hr) were considered. The following objective functions φ j (weighted impact function) were then minimized using linear combinations of the given weight ( w ij ; Chiandussi et al, ):

φ_{j} = \sum_{i = 1}^{5} w_{ij} F_{ni}

where w ij is the weight given to the output variable as shown in Table . i indicates the output variable (compressor energy consumption; fan energy consumption; weight loss; heat and cold shortening duration, and cooling time); and j represents the different scenarios for the objective function (equal weight for all outputs or aimed at saving energy, maximizing quality, or maximizing cooling rate) as shown in Table .…”

Section: Methodsmentioning

confidence: 99%

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Optimizing precooling of large beef carcasses using a comprehensive computational fluid dynamics model

Delele

Kuffi

Smet

et al. 2019

J Food Process Engineering

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Precooling has been questioned as a suitable step in the process of beef carcass cooling. Model‐based optimization was performed to identify optimum operating conditions for different heavy‐muscled beef carcass cooling practices in slaughterhouses with both precooling and cooling stages. The study was conducted using a validated computational fluid dynamics model of the beef carcass cooling process. The precooling practice was optimized based on a weighted impact function taking into account energy consumption, weight loss, cooling time, and heat shortening duration. The values of these output variables were dependent on air temperature, air velocity, and precooling time. The results clearly show the benefit of using a precooling unit that operates with an optimum precooling time, cooling air temperature, and velocity. Using a weighted impact function of energy cost and quality, a precooling time of 4 hr using −30°C but low air velocity (0.58 m s−1) appeared more beneficial than precooling using high airflow fans with high energy consumption. The eventual optimum operation conditions depend on the impact variable that the operator wants to minimize and is a trade‐off between adverse effects on energy use and meat quality. Practical applications The comprehensive computational fluid dynamics model can be applied to optimize the operation and design of carcass precooling system. Carcass cooling system operators can make a choice of the impact variable they want to minimize and use the approach to determine the optimum operating condition of the cooling system. The approach can be applied to develop carcass cooling procedure that could potentially minimize the energy consumption and maximize the quality of the carcass.

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“…For the purpose of balancing the magnitude and unit differences, each output variable F was normalized as (Chiandussi et al, ):

F_{n} = \frac{F ((),,, p_{1}, p_{2}, p_{3}) - F {(p_{1}, p_{2}, p_{3})}_{\min}}{F {(p_{1}, p_{2}, p_{3})}_{\max} - F {(p_{1}, p_{2}, p_{3})}_{\min}}

Section: Methodsmentioning

confidence: 99%

φ_{j} = \sum_{i = 1}^{5} w_{ij} F_{ni}

Section: Methodsmentioning

confidence: 99%

Optimizing precooling of large beef carcasses using a comprehensive computational fluid dynamics model

Delele

Kuffi

Smet

et al. 2019

J Food Process Engineering

View full text Add to dashboard Cite

show abstract

“…3(b). This method determined the closest solution in the objective function space from the goal values of the objective functions by minimizing a scalarized objective function called a global criterion [21]. The particular global criterion employed in this study was:…”

Section: Objective Functionsmentioning

confidence: 99%

Design optimization of an angular contact ball bearing for the main shaft of a grinder

Kim

Kang

Yoon³

et al. 2016

Mechanism and Machine Theory

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“…Our problem is to find the optimum allocation of the agent‐behavior pairs. Many optimization algorithms for convex and nonconvex problems exist; however, our approach requires high‐value solutions at real‐time rates instead of guaranteed optimality . Our target is to allocate the agent‐behavior pairs as fast as possible; therefore, we use a heuristic search to allocate the agents to events according to the distribution expectation ( A ), expected occupancy ( o ), behaviors ( b ), and the set of participating agents ( G ).…”

Section: Proposed Frameworkmentioning

confidence: 99%

Coupling agent motivations and spatial behaviors for authoring multiagent narratives

Zhang

Schaumann

Haworth

et al. 2019

Computer Animation & Virtual

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Authoring behavior narratives for heterogeneous multiagent virtual humans engaged in collaborative, localized, and task‐based behaviors can be challenging. Traditional behavior authoring frameworks are either space‐centric, where occupancy parameters are specified; behavior‐centric, where multiagent behaviors are defined; or agent‐centric, where desires and intentions drive agents' behavior. In this paper, we propose to integrate these approaches into a unique framework to author behavior narratives that progressively satisfy time‐varying building‐level occupancy specifications, room‐level behavior distributions, and agent‐level motivations using a prioritized resource allocation system. This approach can generate progressively more complex and plausible narratives that satisfy spatial, behavioral, and social constraints. Possible applications of this system involve computer gaming and decision‐making in engineering and architectural design.

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Comparison of multi-objective optimization methodologies for engineering applications

Cited by 368 publications

References 10 publications

Optimizing precooling of large beef carcasses using a comprehensive computational fluid dynamics model

Optimizing precooling of large beef carcasses using a comprehensive computational fluid dynamics model

Design optimization of an angular contact ball bearing for the main shaft of a grinder

Coupling agent motivations and spatial behaviors for authoring multiagent narratives

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