Kang-Seok Lee

Bio: Kang-Seok Lee is an academic researcher from Hanyang University. The author has contributed to research in topics: Seismic retrofit & Flexural strength. The author has an hindex of 13, co-authored 57 publications receiving 3484 citations. Previous affiliations of Kang-Seok Lee include National Institute of Standards and Technology & Chonnam National University.

The harmony search heuristic algorithm for discrete structural optimization

Abstract: Many methods have been developed and are in use for structural size optimization problems, in which the cross-sectional areas or sizing variables are usually assumed to be continuous. In most practical structural engineering design problems, however, the design variables are discrete. This paper proposes an efficient optimization method for structures with discrete-sized variables based on the harmony search (HS) heuristic algorithm. The recently developed HS algorithm was conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. In this article, a discrete search strategy using the HS algorithm is presented in detail and its effectiveness and robustness, as compared to current discrete optimization methods, are demonstrated through several standard truss examples. The numerical results reveal that the proposed method is a powerful search and design optimization tool f...

Application of Harmony Search to Vehicle Routing

Abstract: A phenomenon-inspired meta-heuristic algorithm, harmony search, imitating music improvisation process, is introduced and applied to vehicle routing problem, then compared with one of the popular evolutionary algorithms, genetic algorithm. The harmony search algorithm conceptualized a group of musicians together trying to search for better state of harmony. This algorithm was applied to a test traffic network composed of one bus depot, one school and ten bus stops with demand by commuting students. This school bus routing example is a multi-objective problem to minimize both the number of operating buses and the total travel time of all buses while satisfying bus capacity and time window constraints. Harmony search could find good solution within the reasonable amount of time and computation.

Flow and Compressive Strength of Alkali-Activated Mortars

Abstract: The authors present results of testing performed on 18 fly ash (FA)-based mortars and 36 slag-based mortars activated by sodium hydroxide and/or sodium silicate powders. Activator sodium oxide mixing ratio to source materials, fine aggregate-binder ratio (s/b), and water-binder ratio (w/b) are the main variables investigated. That much higher compressive strength and slightly less flow were exhibited by slag-based alkali-activated (AA) mortars when compared to FA-based AA mortars for the same mixing condition was shown by test results. For AA mortar initial flow and 28-day compressive strength evaluation, nonlinear multiple regression analysis developed feed-forward neural networks and simplified equations were developed. A comprehensive database of the results of 82 tests of sodium silicate activated mortars was used for neural network training and testing and simplified equation calibration. Estimation of slag-based AA mortar compressive strength development was also done using the ACI 209R specified formula calibrated against the collected database. Test results were in good agreement with predictions obtained from developed simplified equations and trained neural network, although there was slight overestimation of slag-based AA mortar early strength by the proposed simplified equations.

Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems

Abstract: A new efficient optimization method, called 'Teaching-Learning-Based Optimization (TLBO)', is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the 'Teacher Phase' and the second part consists of the 'Learner Phase'. 'Teacher Phase' means learning from the teacher and 'Learner Phase' means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems.

Moth-flame optimization algorithm

Abstract: In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a useless/deadly spiral path around artificial lights. This paper mathematically models this behaviour to perform optimization. The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems. The statistical results on the benchmark functions show that this algorithm is able to provide very promising and competitive results. Additionally, the results of the real problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces. The paper also considers the application of the proposed algorithm in the field of marine propeller design to further investigate its effectiveness in practice. Note that the source codes of the MFO algorithm are publicly available at http://www.alimirjalili.com/MFO.html.