3,623 research outputs found
An introduction of Krill Herd algorithm for engineering optimization
No full textA new metaheuristic optimization algorithm, called Krill Herd (KH), has been recently proposed by Gandomi and Alavi (2012). In this study, KH is introduced for solving engineering optimization problems. For more verification, KH is applied to six design problems reported in the literature. Further, the performance of the KH algorithm is compared with that of various algorithms representative of the state-of-the-art in the area. The comparisons show that the results obtained by KH are better than the best solutions obtained by the existing methods
Erratum for “Bilevel Data-Driven Modeling Framework for High-Dimensional Structural Optimization under Uncertainty Problems” by Subhrajit Dutta and Amir H. Gandomi
No full textThe authors have identified two typographical errors in Fig. 1 and Table 4 of the original paper. In Fig. 1, an error exists in the height and base width of the 244-member steel transmission tower, while in Table 4 an error exists in the thicknesses of the optimum angle sections. It must be noted that these typographical errors are inconsequential to the published results and do not alter the conclusions of the published paper. The corrected Fig. 1 and Table 4 are provided herein. (Table Presented)
Chaotic Krill Herd algorithm
No full textRecently, Gandomi and Alavi proposed a meta-heuristic optimization algorithm, called Krill Herd (KH). This paper introduces the chaos theory into the KH optimization process with the aim of accelerating its global convergence speed. Various chaotic maps are considered in the proposed chaotic KH (CKH) method to adjust the three main movements of the krill in the optimization process. Several test problems are utilized to evaluate the performance of CKH. The results show that the performance of CKH, with an appropriate chaotic map, is better than or comparable with the KH and other robust optimization approaches. © 2014 Elsevier Inc. All rights reserved
Extending Boundary Updating Approach for Constrained Multi-objective Optimization Problems
No full textTo date, several algorithms have been proposed to deal with constrained optimization problems, particularly multi-objective optimization problems (MOOPs), in real-world engineering. This work extends the 2020 study by Gandomi & Deb on boundary updating (BU) for the MOOPs. The proposed method is an implicit constraint handling technique (CHT) that aims to cut the infeasible search space, so the optimization algorithm focuses on feasible regions. Furthermore, the proposed method is coupled with an explicit CHT, namely, feasibility rules and then the search operator (here NSGA-II) is applied to the optimization problem. To illustrate the applicability of the proposed approach for MOOPs, a numerical example is presented in detail. Additionally, an evaluation of the BU method was conducted by comparing its performance to an approach without the BU method while the feasibility rules (as an explicit CHT) work alone. The results show that the proposed method can significantly boost the solutions of constrained multi-objective optimization
Multi-population Evolutionary and Swarm Intelligence Dynamic Optimization Algorithms: A Survey
No full textMulti-population evolutionary and swarm intelligence dynamic optimization algorithms are the most flexible and effective methods for solving dynamic optimization problems. In a dynamic optimization problem, the search space is affected by environmental changes over time. In multi-population evolutionary and swarm intelligence dynamic optimization algorithms, the number of subpopulations is a parameter determined either by the user or adaptively. The use of multiple sub-populations enables these methods to efficiently track the moving optimum. These methods are capable of gathering historical knowledge about the search space, which is used to effectively react to changes and provide a warmed-up start for the algorithm in new environments. In this chapter, the components of multi-population algorithms are classified to the ones that are used for subpopulation formation, management of computational resources, transmission of information from previous environments, and handling diversity loss. Based on this classification, researchers can have a better understanding of how these components make evolutionary and swarm intelligence algorithms capable of addressing the challenges of dynamic optimization problems
Particle Swarm Optimization Variants for Solving Geotechnical Problems: Review and Comparative Analysis
No full text© 2020, CIMNE, Barcelona, Spain. Optimization techniques have drawn much attention for solving geotechnical engineering problems in recent years. Particle swarm optimization (PSO) is one of the most widely used population-based optimizers with a wide range of applications. In this paper, we first provide a detailed review of applications of PSO on different geotechnical problems. Then, we present a comprehensive computational study using several variants of PSO to solve three specific geotechnical engineering benchmark problems: the retaining wall, shallow footing, and slope stability. Through the computational study, we aim to better understand the algorithm behavior, in particular on how to balance exploratory and exploitative mechanisms in these PSO variants. Experimental results show that, although there is no universal strategy to enhance the performance of PSO for all the problems tackled, accuracies for most of the PSO variants are significantly higher compared to the original PSO in a majority of cases
Enhancing Differential Evolution Algorithm: Adaptation for CEC 2017 and CEC 2021 Test Suites
No full textDifferential evolution (DE) has proved its significance for optimizing various real-world applications and standard benchmarks. In this work, a self-adaptive version of DE is proposed namely LSHADESPA by employing three major modifications, i) proportional shrinking population mechanism for reducing computational burden, ii) simulated annealing-based scaling factor (F) for improving the exploration properties, and iii) oscillating inertia weight-based crossover rate (CR) for a balancing exploitation and exploration. The proposed algorithm has been experimentally tested on IEEE CEC 2017 and IEEE CEC 2021 benchmarks. For performance evaluation, a comparison with respect to JADE, SaDE, SHADE, LSHADE, MVMO, and others has been performed. Experimental and statistical results affirm the superior performance of the proposed LSHADESPA algorithms with respect to other algorithms.No Full Tex
Marine Predators Algorithm: A nature-inspired metaheuristic
No full text© 2020 This paper presents a nature-inspired metaheuristic called Marine Predators Algorithm (MPA) and its application in engineering. The main inspiration of MPA is the widespread foraging strategy namely Lévy and Brownian movements in ocean predators along with optimal encounter rate policy in biological interaction between predator and prey. MPA follows the rules that naturally govern in optimal foraging strategy and encounters rate policy between predator and prey in marine ecosystems. This paper evaluates the MPA's performance on twenty-nine test functions, test suite of CEC-BC-2017, randomly generated landscape, three engineering benchmarks, and two real-world engineering design problems in the areas of ventilation and building energy performance. MPA is compared with three classes of existing optimization methods, including (1) GA and PSO as the most well-studied metaheuristics, (2) GSA, CS and SSA as almost recently developed algorithms and (3) CMA-ES, SHADE and LSHADE-cnEpSin as high performance optimizers and winners of IEEE CEC competition. Among all methods, MPA gained the second rank and demonstrated very competitive results compared to LSHADE-cnEpSin as the best performing method and one of the winners of CEC 2017 competition. The statistical post hoc analysis revealed that MPA can be nominated as a high-performance optimizer and is a significantly superior algorithm than GA, PSO, GSA, CS, SSA and CMA-ES while its performance is statistically similar to SHADE and LSHADE-cnEpSin. The source code is publicly available at: https://github.com/afshinfaramarzi/Marine-Predators-Algorithm, http://built-envi.com/portfolio/marine-predators-algorithm/, https://www.mathworks.com/matlabcentral/fileexchange/74578-marine-predators-algorithm-mpa, and http://www.alimirjalili.com/MPA.html
- Marble slab with a Persian inscription of Jahāngīr dated AH 1027
No full textMarble slab with a Persian inscription of Jahāngīr dated AH 102
Marine predator inspired naked mole-rat algorithm for global optimization
No full textThis paper proposes a hybrid version of marine predator algorithm (MPA) and naked mole-rat algorithm (NMRA) to aggregate the strengths of both algorithms. The new proposed algorithm is named as MpNMRA and designed to overcome the inherent drawbacks of MPA (slow exploitation) and NMRA (limited exploration). The algorithm adds the basic structure of MPA to the worker phase of NMRA, while keeping all the major parameters of both the algorithm. The major parameters of both the algorithms are subjected to four different mutation strategies namely, exponential, linear, simulated annealing and logarithmic mutation strategies. The concept of simulated annealing-based mutation is found to be best for most of the parameters, whereas in some cases exponentially decreasing weights provide better results. Leveraging on the best mutation strategies for all the parameters, the proposed MpNMRA is tested on CEC2005, CEC2014 and CEC 2019 benchmark problems. The experimental results demonstrate that MpNMRA provides best results when compared to other algorithms in the literature on higher-dimensional problems. This work also considers solving three real-world optimization problems and training of a multi-layer perceptron using the proposed algorithm. Statistical results obtained from Wilcoxon's rank-sum test, Freidman's test and computational complexity further proves that the proposed algorithm is highly efficient and provide superior results.
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