Henry gas solubility optimization: A novel physics-based algorithm

The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems

This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

Dwarf Mongoose Optimization Algorithm

This paper proposes a novel global optimization algorithm called Political Optimizer (PO), inspired by the multi-phased process of politics. PO is the mathematical mapping of all the major phases of politics such as constituency allocation, party switching, election campaign, inter-party election, and parliamentary affairs. The proposed algorithm assigns each solution a dual role by logically dividing the population into political parties and constituencies, which facilitates each candidate to update its position with respect to the party leader and the constituency winner. Moreover, a novel position updating strategy called recent past-based position updating strategy (RPPUS) is introduced, which is the mathematical modeling of the learning behaviors of the politicians from the previous election. The proposed algorithm is benchmarked with 50 unimodal, multimodal, and fixed dimensional functions against 15 state of the art algorithms. We show through experiments that PO has an excellent convergence speed with good exploration capability in early iterations. Root cause of such behavior of PO is incorporation of RPPUS and logical division of the population to assign dual role to each candidate solution. Using Wilcoxon rank-sum test, PO demonstrates statistically significant performance over the other algorithms. The results show that PO outperforms all other algorithms, and consistency in performance on such a comprehensive suite of benchmark functions proves the versatility of the algorithm. Furthermore, experiments demonstrate that PO is invariant to function shifting and performs consistently in very high dimensional search spaces. Finally, the applicability on real-world applications is demonstrated by efficiently solving four engineering optimization problems.

Political Optimizer: A novel socio-inspired meta-heuristic for global optimization

Satin bowerbird optimizer

Poor and rich optimization algorithm: A new human-based and multi populations algorithm

Development effort is one of the most important metrics that must be estimated in order to design the plan of a project. The uncertainty and complexity of software projects make the process of effort estimation difficult and ambiguous. Analogy-based estimation (ABE) is the most common method in this area because it is quite straightforward and practical, relying on comparison between new projects and completed projects to estimate the development effort. Despite many advantages, ABE is unable to produce accurate estimates when the importance level of project features is not the same or the relationship among features is difficult to determine. In such situations, efficient feature weighting can be a solution to improve the performance of ABE. This paper proposes a hybrid estimation model based on a combination of a particle swarm optimization (PSO) algorithm and ABE to increase the accuracy of software development effort estimation. This combination leads to accurate identification of projects that are similar, based on optimizing the performance of the similarity function in ABE. A framework is presented in which the appropriate weights are allocated to project features so that the most accurate estimates are achieved. The suggested model is flexible enough to be used in different datasets including categorical and non-categorical project features. Three real data sets are employed to evaluate the proposed model, and the results are compared with other estimation models. The promising results show that a combination of PSO and ABE could significantly improve the performance of existing estimation models.

A PSO-based model to increase the accuracy of software development effort estimation

The estimation of software development effort has been centralized mostly on the accuracy of estimates through dealing with heterogeneous datasets regardless of the fact that the software projects are inherently complex and uncertain. In particular, Analogy Based Estimation (ABE), as a widely accepted estimation method, suffers a great deal from the problem of inconsistent and non-normal datasets because it is a comparison-based method and the quality of comparisons strongly depends on the consistency of projects. In order to overcome this problem, prior studies have suggested the use of weighting methods, outlier elimination techniques and various types of soft computing methods. However the proposed methods have reduced the complexity and uncertainty of projects, the results are not still convincing and the methods are limited to a special domain of software projects, which causes the generalization of methods to be impossible. Localization of comparison and weighting processes through clustering of projects is the main idea behind this paper. A hybrid model is proposed in which the software projects are divided into several clusters based on key attributes (development type, organization type and development platform). A combination of ABE and Particle Swarm Optimization (PSO) algorithm is used to design a weighting system in which the project attributes of different clusters are given different weights. Instead of comparing a new project with all the historical projects, it is only compared with the projects located in the related clusters based on the common attributes. The proposed method was evaluated through three real datasets that include a total of 505 software projects. The performance of the proposed model was compared with other well-known estimation methods and the promising results showed that the proposed localization can considerably improve the accuracy of estimates. Besides the increase in accuracy, the results also certified that the proposed method is flexible enough to be used in a wide range of software projects.

Vahid Khatibi Bardsiri

Papers

The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems

Satin bowerbird optimizer

Poor and rich optimization algorithm: A new human-based and multi populations algorithm

A PSO-based model to increase the accuracy of software development effort estimation

A flexible method to estimate the software development effort based on the classification of projects and localization of comparisons