Akbar Banitalebi

Enhanced compact artificial bee colony

Challenges in many real-world optimization problems arise from limited hardware availability, particularly when the optimization must be performed on a device whose hardware is highly restricted due to cost or space. This paper proposes a new algorithm, namely Enhanced compact Artificial Bee Colony (EcABC) to address this class of optimization problems. The algorithm benefits from the search logic of the Artificial Bee Colony (ABC) algorithm, and similar to other compact algorithms, it does not store the actual population of tentative solutions. Instead, EcABC employs a novel probabilistic representation of the population that is introduced in this paper. The proposed algorithm has been tested on a set of benchmark functions from the CEC2013 benchmark suite, and compared against a number of algorithms including modern compact algorithms, recent population-based ABC variants and some advanced meta-heuristics. Numerical results demonstrate that EcABC significantly outperforms other state of the art compact algorithms. In addition, simulations also indicate that the proposed algorithm shows a comparative performance when compared against its population-based versions.

A self-adaptive binary differential evolution algorithm for large scale binary optimization problems

A new binary variant of the DE algorithm is presented.A new approach to design search strategies for the binary DE algorithms is suggested.The proposed algorithm is implemented and tested on modern benchmark problems and high dimensional knapsack problems.The performance of the proposed algorithm is compared against some recently presented binary algorithms. This study proposes a new self-adaptive binary variant of a differential evolution algorithm, based on measure of dissimilarity and named SabDE. It uses an adaptive mechanism for selecting how new trial solutions are generated, and a chaotic process for adapting parameter values. SabDE is compared against a number of existing state of the art algorithms, on a set of benchmark problems including high dimensional knapsack problems with up to 10,000 dimensions as well as on the 15 learning based problems of the Congress on Evolutionary Computation (CEC 2015). Experimental results reveal that the proposed algorithm performs competitively and in some cases is superior to the existing algorithms.

Modelling and optimization for palm oil plantation management

In this paper, the problem of palm oil plantation management is considered. A non-linear mathematical model is proposed considering two state variables as the density of the young palm oil trees and the part of biomass that can produce oil. In the modelling process, it is assumed that the rate of planting new young trees and the rate of felling inefficient trees can be controlled. It is further assumed that the oil production rate is directly proportional to the biomass of palm oil plantation. A system of delay differential equations is developed to study the behaviour of palm oil plantation. The resulting optimal control problem is also solved to estimate the control variables while the objective is to maintain the biomass at a certain level and maximize the oil palm production in a long period. Numerical simulations are given to illustrate the results.

Solving Optimal Control Problem Using Max-Min Ant System

An improved ant colony algorithm for optimal control problems with box constrain on control functions is presented. The hypercube of the feasible controls as well as the time interval are initially discretized to approximate control problem into a discrete parameter selection problem. Then, the ant colony algorithm is applied to search for optimum parameters of approximated problem while a proper local search is also introduced to iteratively enhance the quality of solution. The results of numerical simulation on MATLAB environment illustrate the effectiveness of this method.

Modeling of microbial approach in wastewater treatment process: A case study of mPHO in Taman Timor oxidation pond, Johor, Malaysia

In this study, we consider the application of biological based product mPHO that contains Phototrophic bacteria (PSB) for the degradation of bacteria Coliform (pollutant) in Taman Timor Oxidation Pond, Johor, Malaysia. A mathematical model is developed to describe the reaction between microorganism and pollutant. The model facilitates the determination of mPHO optimum amount for achieving the maximum pollutant decontamination in the oxidation pond. A partial differential equation model with coupled equation is developed, and the parameters of the model are estimated using the real data collected from the oxidation pond under study. The numerical simulations are also presented to illustrate the performance of proposed model.

A hybrid optimization method for constrained optimal control problem

A new hybrid algorithm by integrating a nested partitions (NP) method with successive quadratic programming (SQP) is presented for global optimization of general optimal control problems involving lumped parameter system. The control parameterization technique first employed to reduce the control problem into a parameter selection problem. Then, in the global phase a vicinity of global optimizer is approximated by an appropriate NP method. Subsequently, the SQP algorithm in the local phase promotes the accuracy of final solution. The effectiveness of the hybrid NP-SQP algorithm is also illustrated by means of numerical simulations. I. INTRODUCTION In the early years of optimal control theory traditional methods based on calculus of variations could furnish some relatively simple problems with an analytical solution. As the analytical solution might not always be available, researchers have been interested in computational method for optimal control problems, yet when reliable and powerful computers became everywhere available, it was the end of an era. The software environments have become a platform for implementation of many practical methods to solve arising control problems. The iterative traditional methods both directly (3) or indirectly (using the Pontryagins minimum principle) (8) rely on gradient information, and perform efficiently for convex problems, otherwise the performance of these methods highly depend on selected initial solution. As an alternative to approximate a solution for complex nonconvex problems, many metaheuristic methods have been proposed (4). In general, these methods iteratively sample amongst potential solutions, and the search is directed using the value of the objective function only, hence reducing the need for gradient information. Moreover, as metaheuristic methods globally search for optima, they are usually characterized as global optimizer due to their capability to efficiently arrive at the vicinity of a global optimum. Nested partitions (NP) (10) is an exemplary method in this category, where concisely described in the third section. We use this method to globally search for optimal control for the class of the control problems described in the next section. Then, in the subsequent sections we propose the hybrid NP-SQP algorithm. II. THE CONTROL PROBLEMS In this study, we consider a general optimal control problem where the system of dynamics is described by a first-order ordinary differential equation (ODE),