Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.

Recent advances in differential evolution – An updated survey

This paper presents the interior search algorithm (ISA) as a novel method for solving optimization tasks. The proposed ISA is inspired by interior design and decoration. The algorithm is different from other metaheuristic algorithms and provides new insight for global optimization. The proposed method is verified using some benchmark mathematical and engineering problems commonly used in the area of optimization. ISA results are further compared with well-known optimization algorithms. The results show that the ISA is efficiently capable of solving optimization problems. The proposed algorithm can outperform the other well-known algorithms. Further, the proposed algorithm is very simple and it only has one parameter to tune.

Interior search algorithm (ISA): a novel approach for global optimization.

Chaos-enhanced accelerated particle swarm optimization

Metamodeling is becoming a rather popular means to approximate the expensive simulations in today’s complex engineering design problems since accurate metamodels can bring in a lot of benefits. The metamodel accuracy, however, heavily depends on the locations of the observed points. Adaptive sampling, as its name suggests, places more points in regions of interest by learning the information from previous data and metamodels. Consequently, compared to traditional space-filling sampling approaches, adaptive sampling has great potential to build more accurate metamodels with fewer points (simulations), thereby gaining increasing attention and interest by both practitioners and academicians in various fields. Noticing that there is a lack of reviews on adaptive sampling for global metamodeling in the literature, which is needed, this article categorizes, reviews, and analyzes the state-of-the-art single−/multi-response adaptive sampling approaches for global metamodeling in support of simulation-based engineering design. In addition, we also review and discuss some important issues that affect the success of an adaptive sampling approach as well as providing brief remarks on adaptive sampling for other purposes. Last, challenges and future research directions are provided and discussed.

A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design

Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives

Development of CFRP Monocoque Front Impact Attenuator for FSAE with VaRTM

This paper proposes a new technique for particle swarm optimization called adaptive range particle swarm optimization (ARPSO). In this technique an active search domain range is determined by utilizing the mean and standard deviation of each design variable. In the initial search stage, the search domain is explored widely. Then the search domain is shrunk so that it is restricted to a small domain while the search continues. To achieve these search processes, new parameters to determine the active search domain range are introduced. These parameters gradually increase as the search continues. Through these processes, it is possible to shrink the active search domain range. Moreover, by using the proposed method, an optimum solution is attained with high accuracy and a small number of function evaluations. Through numerical examples, the effectiveness and validity of ARPSO are examined.

Adaptive Range Particle Swarm Optimization

Abstract Optimum segmented blank holder shape and its variable blank holder gaps (VBHGs) are determined by a sequential approximate optimization (SAO) with radial basis function network. In deep drawing, wrinkling and tearing of blank sheet are major defects. The optimum segmented blank holder shape and its VBHGs are determined to avoid these defects. The Forming Limit Diagram (FLD) is employed to evaluate quantitatively the wrinkling and the tearing. In the numerical examples, a square cup deep drawing is handled. The objective is to minimize the thickness deviation after sheet forming. The wrinkling and the tearing are separately evaluated as the constraints. The dimensions of the segmented blank holder shape and the BHGs are taken as the design variables. The optimization result shows that simultaneous optimization of both the segmented blank holder shape and the VBHGs is one of the effective approaches for improving product quality.

Optimization of Segmented Blank Holder Shape and Its Variable Blank Holder Gap in Deep-Drawing Process

http://dspace.lib.kanazawa-u.ac.jp/dspace/bitstream/2297/25218/1/TE-PR-KITAYAMA-S-1617.pdf

814 The Square Cup Deep Drawing by Variable Blank Holder Force

Statistical analysis is necessary in evaluating the fatigue life of structures. In the present study, rotating bending fatigue tests were carried out on specimens with two small blind holes of a 5052 A1 alloy in order to investigate the distribution characteristics of the fatigue crack growth life based on the small-crack growth law dl/dN= C1σnal. The crack growth rate in each specimen was determined uniquely by σnal, therefore the crack growth life can be predicted by the small-crack growth law. On the assumption that the value of n is a fixed one and the value of C1 is a random variable, the distribution of the crack growth rate can be evaluated through the value of C3 in the relationship dl/dN=C3l. C3 follows a Weibull distribution approximately. The calculated distribution of the ocracy growth life based on the small-crack growth law and the distribution of C3 was in good agreement with the experimental results.

Satoshi Kitayama

Papers

Development of CFRP Monocoque Front Impact Attenuator for FSAE with VaRTM

Adaptive Range Particle Swarm Optimization

Optimization of Segmented Blank Holder Shape and Its Variable Blank Holder Gap in Deep-Drawing Process

814 The Square Cup Deep Drawing by Variable Blank Holder Force

Statistical investigation of the fatigue life based on small-crack growth law