Multi-objective optimization using genetic algorithms: A tutorial

Traditional engineering instruction is deductive, beginning with theories and progressing to the applications of those theories Alternative teaching approaches are more inductive Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established This study reviews several of the most commonly used inductive teaching methods, including inquiry learning, problem-based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching The paper defines each method, highlights commonalities and specific differences, and reviews research on the effectiveness of the methods While the strength of the evidence varies from one method to another, inductive methods are consistently found to be at least equal to, and in general more effective than, traditional deductive methods for achieving a broad range of learning outcomes

Inductive Teaching and Learning Methods: Definitions, Comparisons, and Research Bases

http://romisatriawahono.net/lecture/rm/survey/softcomputing/Boussaid%20-%20Optimization%20Metaheuristics%20-%202013.pdf

A survey on optimization metaheuristics

In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

/pdf/a-review-on-genetic-algorithm-past-present-and-future-2o5y8ru178.pdf

A review on genetic algorithm: past, present, and future

A review on simulation-based optimization methods applied to building performance analysis

The green vehicle routing and scheduling problem (GVRSP) aims to minimize green-house gas emissions in logistics systems through better planning of deliveries/pickups made by a fleet of vehicles. We define a new mixed integer liner programming (MIP) model which considers heterogeneous vehicles, time-varying traffic congestion, customer/vehicle time window constraints, the impact of vehicle loads on emissions, and vehicle capacity/range constraints in the GVRSP. The proposed model allows vehicles to stop on arcs, which is shown to reduce emissions up to additional 8% on simulated data. A hybrid algorithm of MIP and iterated neighborhood search is proposed to solve the problem.

The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion

In information security education, learning experiences that involve hands-on experimentation are extremely important. However, information security topics are challenging to teach in traditional computer laboratories mainly due to restrictive information technology policies. In the literature, virtual computer laboratories have been proposed to address the challenges of providing students with hands-on learning experiences in information security. While the literature mainly focuses on technical aspects of virtual computer laboratories and related hands-on activities, pedagogical aspects of hands-on activities are overlooked. Our experiences with a virtual computer laboratory have shown that hands-on activities which are designed based on a prescriptive, step-by-step approach do not always achieve the expected learning outcomes. In this paper, we propose Kolb's Experiential Learning Cycle as a framework to design hands-on activities in virtual computer laboratories, and we argue that hands-on activities designed based on this framework enhance student learning outcomes. We illustrate how the stages of Kolb's model can be incorporated into hands-on activities and present results from two empirical studies to test the effectiveness of the proposed framework. The empirical findings in the first study suggest that hands-on activities designed based on the proposed framework are more likely to increase student interest and competency compared to step-by-step hands-on activities. In the second study, the collected data is analyzed using structural equation modeling to determine the relationships among the factors affecting student learning outcomes as a result of hands-on activities. The results of the second study show that student-to-student interaction is an important factor determining student learning experiences. Hands-on learning is studied in virtual computer laboratories.Cookbook activities do not achieve comprehensive learning.To enhance student learning, Kolb's model is proposed to design hands-on activities.The benefits of Kolb's model are investigated in the information security domain.The proposed approach enhances student learning in virtual computer laboratories.

Abdullah Konak

Papers

Multi-objective optimization using genetic algorithms: A tutorial

The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion

Using Kolb's Experiential Learning Cycle to improve student learning in virtual computer laboratories

Estimation of all-terminal network reliability using an artificial neural network

A new mixed integer programming formulation for facility layout design using flexible bays