Dynamics in the Multi-objective Subset Sum: Analysing the Behavior of Population Based Algorithms

Author(s):  
Iulia Maria Comsa ◽  
Crina Grosan ◽  
Shengxiang Yang
Author(s):  
Janga Reddy Manne

Most of the engineering design problems are intrinsically complex and difficult to solve, because of diverse solution search space, complex functions, continuous and discrete nature of decision variables, multiple objectives and hard constraints. Swarm intelligence (SI) algorithms are becoming popular in dealing with these kind of complexities. The SI algorithms being population based random search techniques, use heuristics inspired from nature to enable effective exploration of optimal solutions to complex engineering problems. The SI algorithms derived based on principles of co-operative group intelligence and collective behavior of self-organized systems. This chapter presents key principles of multi-optimization, and swarm optimization for solving multi-objective engineering design problems with illustration through few examples.


Author(s):  
Mian Li

Although Genetic Algorithms (GAs) and Multi-Objective Genetic Algorithms (MOGAs) have been widely used in engineering design optimization, the important challenge still faced by researchers in using these methods is their high computational cost due to the population-based nature of these methods. For these problems it is important to devise MOGAs that can significantly reduce the number of simulation calls compared to a conventional MOGA. We present an improved kriging assisted MOGA, called Circled Kriging MOGA (CK-MOGA), in which kriging metamodels are embedded within the computation procedure of a traditional MOGA. In the proposed approach, the decision as to whether the original simulation or its kriging metamodel should be used for evaluating an individual is based on a new objective switch criterion and an adaptive metamodeling technique. The effect of the possible estimated error from the metamodel is mitigated by applying the new switch criterion. Three numerical and engineering examples with different degrees of difficulty are used to illustrate applicability of the proposed approach. The results show that, on the average, CK-MOGA outperforms both a conventional MOGA and our developed Kriging MOGA in terms of the number of simulation calls.


2014 ◽  
Vol 660 ◽  
pp. 487-491 ◽  
Author(s):  
Lavi R. Zuhal ◽  
Yohanes Bimo Dwianto ◽  
Pramudita Satria Palar

This paper presents the development of multi-objective population-based optimization method, called Non-dominated Sorting Genetic Algorithm II (NSGA-II), to optimize the aerodynamic characteristic of a low Reynolds number airfoil. The optimization is performed by changing the shape of the airfoil to obtain geometry with the best aerodynamic characteristics. The results of the study show that the developed optimization tool, coupled with modified PARSEC parameterization, has yielded optimum airfoils with better aerodynamic characteristics compared to original airfoil. Additionally, it is found that the developed method has better performance compared to similar methods found in literature.


2019 ◽  
Vol 10 (2) ◽  
pp. 37-63
Author(s):  
Dihia Belkacemi ◽  
Mehammed Daoui ◽  
Samia Bouzefrane ◽  
Youcef Bouchebaba

Mapping parallel applications onto a network on chip (NoC) that is based on heterogeneous MPSoCs is considered as an instance of an NP-hard and a multi-objective problem. Various multi-objective algorithms have been proposed in the literature to handle this issue. Metaheuristics stand out as highly appropriate approaches to deal with this kind of problem. These metaheuristics are classified into two sets: population-based metaheuristics and single solution-based ones. To take advantage of the both sets, the trend is to use hybrid solutions that have shown to give better results. In this article, the authors propose to hybridize these two metaheuristics sets to find good Pareto mapping solutions to optimize the execution time and the energy consumption simultaneously. The experimental results have shown that the proposed hybrid algorithms give high quality non-dominated mapping solutions in a reasonable runtime.


Author(s):  
Janga Reddy Manne

Most of the engineering design problems are intrinsically complex and difficult to solve because of diverse solution search space, complex functions, continuous and discrete nature of decision variables, multiple objectives, and hard constraints. Swarm intelligence (SI) algorithms are becoming popular in dealing with these complexities. The SI algorithms, being population-based random search techniques, use heuristics inspired from nature to enable effective exploration of optimal solutions to complex engineering problems. The SI algorithms derived from principles of cooperative group intelligence and collective behavior of self-organized systems. This chapter presents key principles of multi-optimization and swarm optimization for solving multi-objective engineering design problems with illustration through a few examples.


2021 ◽  
Author(s):  
Xinyu Li ◽  
Prajna Kasargodu Anebgailu ◽  
Jörg Dietrich

<p>The calibration of hydrological models using bio-inspired meta-heuristic optimization techniques has been extensively tested to find the optimal parameters for hydrological models. Shuffled frog-leaping algorithm (SFLA) is a population-based cooperative search technique containing virtual interactive frogs distributed into multiple memeplexes. The frogs search locally in each memeplex and are periodically shuffled into new memeplexes to ensure global exploration. Though it is developed for discrete optimization, it can be used to solve multi-objective combinatorial optimization problems as well.</p><p>In this study, a hydrological catchment model, Hydrological Predictions for the Environment (HYPE) is calibrated for streamflow and nitrate concentration in the catchment using SFLA. HYPE is a semi-distributed watershed model that simulates runoff and other hydrological processes based on physical as well as conceptual laws. SFLA with 200 runtimes and 5 memeplexes containing 10 frogs each is used to calibrate 22 model parameters. It is compared with manual calibration and Differential Evolution Markov Chain (DEMC) method from the HYPE-tool. The preliminary results of the statistical performance measures for streamflow calibration show that SFLA has the fastest convergence speed and higher stability when compared with the DEMC method. NSE of 0.68 and PBIAS of 7.72 are recorded for the best run of SFLA during the calibration of streamflow. In comparison, the HYPE-tool DEMC produced the best NSE of 0.45 and a PBIAS of -3.37 while the manual calibration resulted in NSE of 0.64 and PBIAS of 2.01.</p>


2013 ◽  
Vol 46 (9) ◽  
pp. 1572-1599 ◽  
Author(s):  
Ioannis Giagkiozis ◽  
Robin C. Purshouse ◽  
Peter J. Fleming

2016 ◽  
Vol 23 (3) ◽  
pp. 205-220 ◽  
Author(s):  
Zhiwei Yang ◽  
Michael Emmerich ◽  
Thomas Bäck ◽  
Joost Kok ◽  
Zhiwei Yang ◽  
...  

2019 ◽  
Vol 37 (2) ◽  
pp. 753-788
Author(s):  
Slawomir Koziel ◽  
Adrian Bekasiewicz

Purpose The purpose of this paper is to investigate the strategies and algorithms for expedited design optimization of microwave and antenna structures in multi-objective setup. Design/methodology/approach Formulation of the multi-objective design problem-oriented toward execution of the population-based metaheuristic algorithm within the segmented search space is investigated. Described algorithmic framework exploits variable fidelity modeling, physics- and approximation-based representation of the structure and model correction techniques. The considered approach is suitable for handling various problems pertinent to the design of microwave and antenna structures. Numerical case studies are provided demonstrating the feasibility of the segmentation-based framework for the design of real-world structures in setups with two and three objectives. Findings Formulation of appropriate design problem enables identification of the search space region containing Pareto front, which can be further divided into a set of compartments characterized by small combined volume. Approximation model of each segment can be constructed using a small number of training samples and then optimized, at a negligible computational cost, using population-based metaheuristics. Introduction of segmentation mechanism to multi-objective design framework is important to facilitate low-cost optimization of many-parameter structures represented by numerically expensive computational models. Further reduction of the design cost can be achieved by enforcing equal-volumes of the search space segments. Research limitations/implications The study summarizes recent advances in low-cost multi-objective design of microwave and antenna structures. The investigated techniques exceed capabilities of conventional design approaches involving direct evaluation of physics-based models for determination of trade-offs between the design objectives, particularly in terms of reliability and reduction of the computational cost. Studies on the scalability of segmentation mechanism indicate that computational benefits of the approach decrease with the number of search space segments. Originality/value The proposed design framework proved useful for the rapid multi-objective design of microwave and antenna structures characterized by complex and multi-parameter topologies, which is extremely challenging when using conventional methods driven by population-based metaheuristics algorithms. To the authors knowledge, this is the first work that summarizes segmentation-based approaches to multi-objective optimization of microwave and antenna components.


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