A pinboard by
Imran Rahman

Ph.D Candidate, Universiti Sains Malaysia


Bio-inspired computational intelligence techniques for real-world optimization

Fish Swarm Algorithm (FSA) is a underwater bio-inspired algorithms which have currently attracted interest among optimization experts. It is inspired by cooperative hunting behavior such as following other fishes in search for food sources and protecting the group against threats during food hunting. Despite outrageous performance, analyses have affirmed that AFSA is lacking in proper balance between local and global search, leading towards premature convergence. The performance of formulated improved algorithm will be assessed based on various benchmark functions and wide range of applications in engineering field such as power system, telecommunications and power electronics.

As almost all real-world applications require optimization of some parameters, success of the proposed algorithm would eventually serves the society, academia, industry and national economy.


Optimizing dam and reservoirs operation based model utilizing shark algorithm approach

Abstract: Computational intelligence (CI) is a fast evolving area in which many novel algorithms, stemmed from various inspiring sources, were developed during the past decade. Nevertheless, many of them are dispersed in different research directions, and their true potential is thus not fully utilized yet. Therefore, there is a need to investigate the potential of these methods in different engineering optimization problems. In fact, shark algorithm is a stochastic search optimization algorithm which is started first in a set of random generated potential solutions, and then performs the search for the optimum one interactively. Such procedure is appropriate to the system features of the reservoir system as it is a stochastic system in nature. In this article, investigation of the potential of shark algorithm is examined as an optimization algorithm for reservoir operation. To achieve that real single reservoir and multi-reservoir optimal operations have been performed utilizing shark algorithm. Many performances indexes have been measured for each case study utilizing the proposed shark algorithm and another existing optimization algorithms namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The results showed that the proposed shark algorithm outperformed the other algorithms and achieved higher reliability index and lesser vulnerability index. Moreover, standard deviation and coefficient of variation in Shark Algorithm were less than the other two algorithms, which indicates its superiority.

Pub.: 21 Jan '17, Pinned: 24 Aug '17