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.
Abstract: Direct-current microgrids (DC-MGs) can operate in either grid-connected or stand-alone mode. In particular, stand-alone DC-MG has many distinct applications. However, the optimal power flow problem of a stand-alone DC-MG is inherently non-convex. In this paper, the optimal power flow (OPF) problem of DC-MG is investigated considering convex relaxation based on second-order cone programming (SOCP). Mild assumptions are proposed to guarantee the exactness of relaxation, which only require uniform nodal voltage upper bounds and positive network loss. Furthermore, it is revealed that the exactness of SOCP relaxation of DC-MGs does not rely on either topology or operating mode of DC-MGs, and an optimal solution must be unique if it exists. If line constraints are considered, the exactness of SOCP relaxation may not hold. In this regard, two heuristic methods are proposed to give approximate solutions. Simulations are conducted to confirm the theoretic results.
Pub.: 17 Aug '17, Pinned: 26 Aug '17
Abstract: Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.
Pub.: 19 Feb '15, Pinned: 26 Aug '17
Abstract: International Journal of Computational Intelligence and Applications, Ahead of Print. Swarm intelligence algorithms are amongst the most efficient approaches toward solving optimization problems. Up to now, most of swarm intelligence approaches have been proposed for optimization in static environments. However, numerous real-world problems are dynamic which could not be solved using static approaches. In this paper, a novel approach based on artificial fish swarm algorithm (AFSA) has been proposed for optimization in dynamic environments in which changes in the problem space occur in discrete intervals. The proposed algorithm can quickly find the peaks in the problem space and track them after an environment change. In this algorithm, artificial fish swarms are responsible for finding and tracking peaks and several behaviors and mechanisms are employed to cope with the dynamic environment. Extensive experiments show that the proposed algorithm significantly outperforms previous algorithms in most of tested dynamic environments modeled by moving peaks benchmark.
Pub.: 09 Jun '16, Pinned: 26 Aug '17
Abstract: In many practical engineering design problems, the form of objective functions is not given explicitly in terms of design variables. Given the value of design variables, under this circumstance, the value of objective functions is obtained by some analysis such as structural analysis, fluidmechanic analysis, thermodynamic analysis, and so on. Usually, these analyses are considerably time consuming to obtain a value of objective functions. In order to make the number of analyses as few as possible, we suggest a method by which optimization is performed in parallel with predicting the form of objective functions. In this paper, radial basis function networks (RBFN) are employed in predicting the form of objective functions, and genetic algorithms (GA) are adopted in searching the optimal value of the predicted objective function. The effectiveness of the suggested method will be shown through some numerical examples.
Pub.: 01 Jun '02, Pinned: 26 Aug '17
Abstract: In this paper, we revisit the mean-variance model of Markowitz and the construction of the risk-return efficient frontier. A few other models, such as the mean absolute deviation, the minimax and maximin, and models with diagonal quadratic form as objectives, which use alternative metrics for risk are also introduced. Then we present a neurodynamic model for solving these kinds of problems. By employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the original problem. The validity and transient behavior of the neural network are demonstrated by using several examples of portfolio selection.
Pub.: 05 Dec '13, Pinned: 24 Aug '17
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