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In engineering design, optimization methods are frequently used to improve the initial design of a product. However, the selection of an appropriate method is challenging since many methods exist, especially for the case of simulation-based optimization. This paper proposes a systematic procedure to support this selection process. Building upon qua...
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Context 1
... total score of an algorithm is the sum over all scores score i . The developed DM with all criteria, their relations, values for two algorithms, and the bar diagram is shown in Figure 2. ...
Context 2
... quality characteristics comprise several properties of an algorithm. For each criterion, a direction of improvement is provided (see row "Direction of Improvement" in Figure 2). ...
Context 3
... Materials: The following are available online at http://www.mdpi.com/2571-5577/2/3/20/s1, Figure S1: DM for boom configuration optimization before evaluating the results, Figure S2: DM for boom configuration optimization after evaluating the results, Figure S3: DM for plate partitioning before evaluating the results, Figure S4: DM for plate partitioning optimization after evaluating the results, Figure S5: Comparison of cost approximations for metal sheets-total costs, material costs and welding costs; the colors indicate the number of segments, starting at the bottom left with 4 segments (dark blue) to the top right with 23 segments (yellow), Figure S6: Comparison of the two approaches for calculating a plate partitioning, Figure S7: For each of 6250 BTBs with, the ratio of plate lengths of the first to the second last plate lying within 1% of the maximum length is shown. ...
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... Despite the voltage stability could not be maintained for all the buses, the algorithms have brought many congested buses within the stability limits. The need for optimization algorithm is that the problem model, which does has an exact optimal solution that can be determined after huge computational cost [37]. It is well known that the optimal solution can facilitate the process of determining near-optimal solution, rather than exact optimal solution, but with relatively less computational cost [37]. ...
... The need for optimization algorithm is that the problem model, which does has an exact optimal solution that can be determined after huge computational cost [37]. It is well known that the optimal solution can facilitate the process of determining near-optimal solution, rather than exact optimal solution, but with relatively less computational cost [37]. Here, the proposed algorithm has brought the congested buses as many as possible, as per Fig. 1. ...
This paper extends our previous algorithm, termed as Particle Swarm Optimization with Distributed Acceleration Constants (PSODAC), which has been introduced for mitigating congestion based on rescheduling in a deregulated environment. The proposed variant adopts a sequential hybridization of the PSODAC principle with Differential Evolution, which is a well-known evolutionary algorithm. The variant in this paper is termed as Sequentially Hybridized Differential Evolution with Particle Swarm Optimization (SH-DEPSO). The experimental investigations are carried out in the IEEE 14 bus system in two scenarios namely, single point congestion and multipoint congestion. Firstly, the performance investigation is carried out on mitigating the congestion using cost analysis, stability analysis, complexity analysis, and strategy analysis. Secondly, the characteristics of the algorithm are observed by performing convergence analysis and investigating the quality of the solution dynamics. The studies demonstrate the competing performance of SH-DEPSO over PSODAC and the traditional PSO.
... Four different evolutionary, nonlinear optimization techniques were considered for optimizing the objective function namely; PSO, GA, PS, and SA. Based on suggested criteria (Doris et al., 2019), the most promising technique was selected for probing the optimization search space to obtain the optimum values of the input parameters that give the maximum PCE. ...
... To select the most compatible global optimization algorithm for our problem, four criteria were considered, namely; the ability of the algorithm to converge to the optimum value, the ability of the algorithm to generate new design alternatives, the computational time required (number of function counts required for convergence), and the reproducibility of the optimum result obtained (Doris et al., 2019). The four algorithms were assessed based on all four criteria and the highest performing option was chosen. ...
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Regression testing remains a promising research area for the last few decades. It is a type of testing that aims at ensuring that recent modifications have not adversely affected the software product. After the introduction of a new change in the system under test, the number of test cases significantly increases to handle the modification. Consequently, it becomes prohibitively expensive to execute all of the generated test cases within the allocated testing time and budget. To address this situation, the test suite reduction (TSR) technique is widely used that focusses on finding a representative test suite without compromising its effectiveness such as fault-detection capability. In this work, a systematic review study is conducted that intends to provide an unbiased viewpoint about TSR based on various types of search algorithms. The study's main objective is to examine and classify the current state-of-the-art approaches used in search-based TSR contexts. To achieve this, a systematic review protocol is adopted and, the most relevant primary studies (57 out of 210) published between 2007 and 2022 are selected. Existing search-based TSR approaches are classified into five main categories, including evolutionary-based, swarm intelligence-based, human-based, physics-based, and hybrid, grounded on the type of employed search algorithm. Moreover, the current work reports the parameter settings according to their category, the type of considered operator(s), and the probabilistic rate that significantly impacts on the quality of the obtained solution. Furthermore, this study describes the comparison baseline techniques that support the empirical comparison regarding the cost-effectiveness of a search-based TSR approach. Finally, it isconcluded that search-based TSR has great potential to optimally solve the TSR problem. In this regard, several potential research directions are outlined as useful for future researchers interested in conducting research in the TSR domain.
This study intended to optimize the process parameters of the friction stir welding (FSW) technique to maximize the mechanical properties of the AA6082/B4C composite weldments. The metal matrix composites are prepared with aluminum alloy 6082 and boron carbide particles of 10µm size by using a stir casting technique. The central composite design technique is applied to design the FSW experiments. The experiments were carried out in butt joint configuration by varying welding transverse speed, axial load, plunge depth, and tool rotational speed. The optimum parameters are estimated by considering output responses such as ultimate tensile strength and microhardness. The effect of process parameters on output responses was analyzed graphically. Two optimization techniques, namely genetic algorithm and desirability function approach, are implemented to identify the optimal values of process parameters. The results obtained by both optimization techniques are in good agreement. The optimum values are validated with experimental results.
