MUCSO: A Modified Chaotic Particle Swarm Optimization with Uniform Initialization for Optimizing Software Effort Estimation  
[PERSON]  
1 Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada,  
Yogyakarta 55281, Indonesia; [EMAIL_ADDRESS] (R.F. ); [EMAIL_ADDRESS] (A.E.P.) 1 Department of Informatics, Faculty of Industrial Technology, Universitas Ahmad Dahlan,  
Yogyakarta 55191, Indonesia  
2  
[PERSON]  
1 Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada,  
Yogyakarta 55281, Indonesia; [EMAIL_ADDRESS] (R.F. ); [EMAIL_ADDRESS] (A.E.P.) 1 Department of Informatics, Faculty of Industrial Technology, Universitas Gadjah Mada,  
Yogyakarta 55281, Indonesia; [EMAIL_ADDRESS] (R.F. ); [EMAIL_ADDRESS] (A.E.P.) 1 Department of Informatics, Faculty of Industrial Technology, Universitas Gadjah Mada,  
Yogyakarta 55191, Indonesia  
2  
[PERSON]  
1 Department of Electrical Engineering and Information Technology, Universitas G  
complex problem, and the attribute relation is difficult to analyse. Therefore, the optimization process plays a crucial role in this regard. Optimization can be directly utilized in the effort estimation process, such as the optimization of attribute weighting in COCOMO, Use Case Points, or Analogy Based Estimation (ABE). Particle swarm optimization (PSO) is one of Swarm intelligence (SI) algorithms alongside Galactic Swarm Optimization (GSO), the Firefly Algorithm (FA), Ant Lion Optimization (ALO), Ant Colony Optimization ACO, Artificial Bee Colony algorithm (ABC), Whale Optimization, Glowworm Swarm Optimization (GSO), and Cuckoo Search Algorithm (CSA). Swarm intelligence has been widely used in hybrid approaches. For instance, the genetic operators have been hybridized with PSO, such as a standard selection mechanism from evolutionary computations [5], the employment of two different crossover operations in order to breed promising exemplars [6], and an adaptive mutation strategy [7]. Similarly, Ant Colony optimization [8], the differential evolution algorithm [9], tabu search [10], Artificial Bee colony [11], Firefly Algorithm [12] and gravitational search algorithm [13] have been incorporated into PSO to take full advantage of each algorithm's strengths. Particle Swarm Optimization (PSO) was introduced in 1995 by [14] and gained a high level of interest since it has three advantages: ease of implementation, the requirement of parameters that are simply tuned, and its effectiveness in identifying the global optimum solution since it has a clearer particle direction [15]. Due to its benefit, PSO is widely used in software effort estimation research [16]. However, it still has some shortcomings, such as premature convergence and being trapped into local optima and a poor global search ability [15]. Premature convergence and poor global search ability are influenced by the inertia weight parameter and acceleration coefficient. Inertia weight (\(\omega\)) can provide particles with a dynamic adjustment ability in different environments, thus realizing the balance between exploration and exploitation. Inertia weight plays an important role in controlling the processes of exploration (global search) and exploitation (local search) by maintaining a balance in their capabilities. From the perspective of statistical analysis, it is believed that the overall performance of PSO is strongly affected by the inertia weight [17]. Researchers have provided various modified versions of PSO to overcome the flaws. Generally speaking, there are six strategies to achieve these goals, such as tuning the following control parameters: inertial weight (\(w\)) [18], cognitive acceleration coefficient (\(c_{1}\)) and social acceleration coefficient (\(c_{2}\)) [19]; hybrid PSO [13]; changing topological structure [20], eliminating the velocity formula [21]; changing the learning strategy [22]; and changing particle initialization strategy [23]. These variants have successfully improved PSO performance.