The following computational tools were conferred:-
1. PSO (particle swarm optimization)
PSO is a computational method that optimizes a problem by iteratively trying to improve a candidate’s solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around the search-space . Each particle's movement is influenced by its local well known position and is also guided towards the best known positions in the search-space, which are updated as better positions and are found by other particles. This is expected to move the swarm toward the best solutions.
2. DE (differential evolution)
DE is a method that optimizes a problem by improving a candidate’s solution with regard to a given quality’s measure. Such methods are commonly known as metaheuristics as they make few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions.
3. Multivariate techniques
It concerns understanding of various objectives and background of each of the different forms of multivariate analysis, and how they correlate each other. The practical implementation of multivariate statistics to a specific problem may involve univariate and multivariate analysis so as to understand the relationships between variables and their relevance to the actual problem.
4. FL (Fuzzy logic)
FL was formulated as a better method for sorting and handling data since it mimics human control logic. It can be built into anything from small, hand-held products to large computerized process control systems. It is very robust and forgiving of operator and data input and often works when first implemented with little or no tuning.
5. NN (Neural networks)
NN is a group of interconnected natural or artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. In more practical terms neural networks are non-linear statistical data modeling or decision making tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data.
6. Ant recognition
Some recent studies have pointed that , the self-organization of neurons into brain-like structures, and the self-organization of ants into a swarm are similar in many respects. If possible to implement, these features could lead to important developments in pattern recognition systems, where perceptive capabilities can emerge and evolve from the interaction of many simple local rules. |
