The Strength Pareto Evaluation Algorithm (SPEA) (Zitzler and Thiele 1999) is one of the prominent technique for approximating the pareto-optimal set for the Multiple Objective Optimization (MOO) algorithm. The Strength Pareto Evaluation Algorithm 2 (SPEA2) is an improved version of SPEA that was introduced in the year 2001. SPEA2 in contrast to SPEA incorporates a fine-grained fitness assignment strategy, an improved archive truncation technique, and a density assessment procedure. In this paper, we studied the influence of the optimization ability of SPEA2 on different benchmark functions by evaluating different performance metrics. The benchmark functions used in the paper include 10 constrained functions (CF?s) and 10 unconstrained funct...
Scope and Method of Study: Over the years, most multiobjective particle swarm optimization (MOPSO) a...
Master of ScienceDepartment of Electrical and Computer EngineeringSanjoy DasStephen M. WelchMulti-ob...
Machine learning models have achieved impressive predictive performance in various applications such...
A framework is proposed that combines separately developed multidisciplinary optimization, multi-obj...
Over recent years, Evolutionary Algorithms have emerged as a practical approach to solve hard optimi...
This study proposes a self-adaptive penalty function algorithm for solving constrained optimization ...
In this thesis, we investigate and develop a number of online learning selection choice function bas...
To overcome the shortcomings of the least squares regression method, two methods - the normal distan...
The representation of 2 Satisfiability problem or 2SAT is increasingly viewed as a significant logic...
The need for computers to make educated decisions is growing. Various methods have been developed fo...
Proteomics is the study of the structure and behavior of proteins. In order to perform this kind of ...
Dr. James Keller, Dissertation Supervisor.Includes vita.Field of study: Electrical and computer engi...
In my dissertation I develop and evaluate methods for gene-mapping that can extract useful informati...
In Particle Swarm Optimization, it has been observed that swarms often stall as opposed to converge....
Immobile location-allocation (LA) problems is a type of LA problem that consists in determining the ...
Scope and Method of Study: Over the years, most multiobjective particle swarm optimization (MOPSO) a...
Master of ScienceDepartment of Electrical and Computer EngineeringSanjoy DasStephen M. WelchMulti-ob...
Machine learning models have achieved impressive predictive performance in various applications such...
A framework is proposed that combines separately developed multidisciplinary optimization, multi-obj...
Over recent years, Evolutionary Algorithms have emerged as a practical approach to solve hard optimi...
This study proposes a self-adaptive penalty function algorithm for solving constrained optimization ...
In this thesis, we investigate and develop a number of online learning selection choice function bas...
To overcome the shortcomings of the least squares regression method, two methods - the normal distan...
The representation of 2 Satisfiability problem or 2SAT is increasingly viewed as a significant logic...
The need for computers to make educated decisions is growing. Various methods have been developed fo...
Proteomics is the study of the structure and behavior of proteins. In order to perform this kind of ...
Dr. James Keller, Dissertation Supervisor.Includes vita.Field of study: Electrical and computer engi...
In my dissertation I develop and evaluate methods for gene-mapping that can extract useful informati...
In Particle Swarm Optimization, it has been observed that swarms often stall as opposed to converge....
Immobile location-allocation (LA) problems is a type of LA problem that consists in determining the ...
Scope and Method of Study: Over the years, most multiobjective particle swarm optimization (MOPSO) a...
Master of ScienceDepartment of Electrical and Computer EngineeringSanjoy DasStephen M. WelchMulti-ob...
Machine learning models have achieved impressive predictive performance in various applications such...