Multiple Design Problems Research Articles

Comparison of conceptually different multi-objective Bayesian optimization methods for material design problems

For real-world applications, material properties must usually meet multiple requirements, and researchers often spend considerable time designing such materials by trial and error. Multi-objective Bayesian optimization (MOBO) constitutes a promising data-driven solution to accelerate such design problems. As things stand, conceptually different MOBO methods exist for material design problems, such as scalarization- and hypervolume-based methods. However, no standard approach exists to compare how these methods perform and the appropriate choice of MOBO method in each case remains unclear. Herein, a benchmark protocol to compare how conceptually different MOBO methods perform was introduced, based on which the performances of MOBO methods were comprehensively compared using multiple design problems and performance metrics. The benchmark results showed that there was no method that performed best for all combinations of design problems and performance metrics. Moreover, when multiple MOBO methods were compared, the opportunity cost of using each method emerged and it was shown that an inappropriately chosen method can hinder MOBO efficiency. The benchmark results shown here highlight the importance of choosing the right MOBO method and provide guidelines for how this can be done.

Code smell detection using multi-label classification approach

Code smells are characteristics of the software that indicates a code or design problem which can make software hard to understand, evolve, and maintain. There are several code smell detection tools proposed in the literature, but they produce different results. This is because smells are informally defined or subjective in nature. Machine learning techniques help in addressing the issues of subjectivity, which can learn and distinguish the characteristics of smelly and non-smelly source code elements (classes or methods). However, the existing machine learning techniques can only detect a single type of smell in the code element that does not correspond to a real-world scenario as a single element can have multiple design problems (smells). Further, the mechanisms proposed in the literature could not detect code smells by considering the correlation (co-occurrence) among them. To address these shortcomings, we propose and investigate the use of multi-label classification (MLC) methods to detect whether the given code element is affected by multiple smells or not. In this proposal, two code smell datasets available in the literature are converted into a multi-label dataset (MLD). In the MLD, we found that there is a positive correlation between the two smells (long method and feature envy). In the classification phase, the two methods of MLC considered the correlation among the smells and enhanced the performance (on average more than 95% accuracy) for the 10-fold cross-validation with the ten iterations. The findings reported help the researchers and developers in prioritizing the critical code elements for refactoring based on the number of code smells detected.

Power control and clustering in heterogeneous cellular networks

Recently, it is widely believed that significant coverage and performance improvement can be achieved through the deployment of small cells in conjunction with the well-established macro cells. However, it is expected that the high density of base stations in such heterogeneous cellular networks will give rise to multiple design problems related to both co-tier (small-to-small) and cross-tier (between small and macro cells) interference. Fortunately, cooperation between base stations will play a major role to cope with these problems and hence to enhance the users’ data rates. In this paper, we consider a two-tier cellular network comprised of a macro cell underlaid with multiple small cells where both co-tier and cross-tier interference are taken into account. We study the scenario where the small cell base stations seek to maximize a common objective by forming multiple clusters through cooperation. These base stations have also to allocate power to their associated users and, at the same time, control the total aggregate interference caused to the macro cell user which has to be kept below a threshold prefixed by the macro cell base station. We consider two utility functions: the overall sum rate of the small cell network and the minimum data rate of the small cell users. We formulate the studied problems as mixed integer nonlinear optimization problems and we discuss their NP hardness. Therefore, due to the complexity of finding the optimal solution, we design heuristic algorithms which resolves efficiently the tradeoff between computational complexity and performance. We show through simulations that the designed heuristics approach the optimal solution (obtained using the complex exhaustive search algorithm) with highly reduced computational complexity.

Bilevel multiobjective packaging optimization for automotive design

The successful application of multiobjective optimization to engineering problems has motivated studies of more complex systems involving multiple subsystems and design disciplines, each with multiple design criteria. Complex system design requires participation of different teams that are highly specialized within each discipline and subsystem. Such a high differentiation results in limited sharing of information among the design teams. The mathematical modeling and the solution algorithm proposed in this paper address the issue of coordinating multiple design problems that negotiate according to conflicting criteria. The design of the layout of hybrid vehicles is formulated as a bilevel decomposed problem including a vehicle level and a battery level in concert with the specialization of the respective design teams required at each level. An iterative algorithm, the Multiobjective Decomposition Algorithm (MODA) is proposed, whose generated sequences are shown to converge to efficient designs for the overall design problem under certain conditions examined in the context of the block coordinate descent method and the method of multipliers. MODA applied to the hybrid electric design problem captures the bilevel tradeoffs originating by the conflicting objectives at the vehicle and battery levels.

On Instantiation and Integration Commutability of Design Pattern

Design patterns capture expert design experience in generic design structure and behavior. To reuse design experience, a design pattern needs to be instantiated from its generic template to the application design in a particular context. It can be integrated with other patterns to solve multiple design problems. The instantiation and integration of design patterns are two important processes when a designer reuses design experience in an application. It is important to know whether the instantiation and integration commute because it can save considerable time and effort of software designers for trial-and-error. In this paper, we investigate the commutability of the instantiation and integration of design patterns. We provide rigorous proofs on the conditions when the order of these two design processes does not matter. Our results allow the software designers to choose the design processes with assurance of their equivalence. The benefits of our work include helping the designers to make informed design decisions based on the convergence of different design processes and reducing the possible design choices, and thus the complexity of software development.

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