Pareto Efficient Multi Objective Optimization for Local Tuning of Analogy Based Estimation

11/29/2016
by   Mohammad Azzeh, et al.
0

Analogy Based Effort Estimation (ABE) is one of the prominent methods for software effort estimation. The fundamental concept of ABE is closer to the mentality of expert estimation but with an automated procedure in which the final estimate is generated by reusing similar historical projects. The main key issue when using ABE is how to adapt the effort of the retrieved nearest neighbors. The adaptation process is an essential part of ABE to generate more successful accurate estimation based on tuning the selected raw solutions, using some adaptation strategy. In this study we show that there are three interrelated decision variables that have great impact on the success of adaptation method: (1) number of nearest analogies (k), (2) optimum feature set needed for adaptation, and (3) adaptation weights. To find the right decision regarding these variables, one need to study all possible combinations and evaluate them individually to select the one that can improve all prediction evaluation measures. The existing evaluation measures usually behave differently, presenting sometimes opposite trends in evaluating prediction methods. This means that changing one decision variable could improve one evaluation measure while it is decreasing the others. Therefore, the main theme of this research is how to come up with best decision variables that improve adaptation strategy and thus, the overall evaluation measures without degrading the others. The impact of these decisions together has not been investigated before, therefore we propose to view the building of adaptation procedure as a multi-objective optimization problem. The Particle Swarm Optimization Algorithm (PSO) is utilized to find the optimum solutions for such decision variables based on optimizing multiple evaluation measures

READ FULL TEXT
research
04/21/2020

A Decomposition-based Large-scale Multi-modal Multi-objective Optimization Algorithm

A multi-modal multi-objective optimization problem is a special kind of ...
research
12/14/2020

Evolutionary Multi-Objective Optimization Algorithm Framework with Three Solution Sets

It is assumed in the evolutionary multi-objective optimization (EMO) com...
research
05/10/2013

Quality Measures of Parameter Tuning for Aggregated Multi-Objective Temporal Planning

Parameter tuning is recognized today as a crucial ingredient when tackli...
research
01/31/2021

Niching Diversity Estimation for Multi-modal Multi-objective Optimization

Niching is an important and widely used technique in evolutionary multi-...
research
02/20/2020

How to Evaluate Solutions in Pareto-based Search-Based Software Engineering? A Critical Review and Methodological Guidance

With modern requirements, there is an increasing tendancy of considering...
research
06/19/2014

Racing Multi-Objective Selection Probabilities

In the context of Noisy Multi-Objective Optimization, dealing with uncer...

Please sign up or login with your details

Forgot password? Click here to reset