Roach infestation optimization

Abstract only availableThere are many function optimization algorithms based on the collective behavior of natural systems — Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are two of the most popular. This poster presents a new adaptation of the PSO algorithm, entitled Roach Infestation Optimization (RIO), which is inspired by recent discoveries in the social behavior of cockroaches. We present the development of the simple behaviors of the individual agents, which emulate some of the discovered cockroach social behaviors. We also describe a "hungry" version of the PSO and RIO, which we aptly call Hungry PSO and Hungry RIO. Comparisons with standard PSO show that Hungry PSO, RIO, and Hungry RIO are all more effective at finding the global optima of a suite of test functions.College of Engineering Undergraduate Research Optio

Development of Discrete-Cockroach Algorithm (DCA) for Feature Selection Optimization

One of the recently proposed algorithms in the field of bio-inspired algorithm is the Hungry Roach Infestation Optimization (HRIO) algorithm. Haven has developed optimization algorithms HRIO that is inspired by recent discoveries in the social behaviour of cockroaches. Result showed that HRIO was effective at finding the global optima of a suite of test functions. However, there is no researcher who has observed HRIO for solving discrete problems. Therefore, we try to develop a discrete-cockroach algorithm (DCA) as the modification of HRIO for solving discrete optimization problem. We test the algorithm to solve bio-computation problem using single and multi-objectives optimization. The results showed DCA has better performance compared to the existed bio-inspired optimization algorithms such as genetic algorithms (GA) and discrete-particle swarm optimization (discrete-PSO)

Roach Infestation Optimization MPPT Algorithm of PV Systems for Adaptive to Fast-Changing Irradiation and Partial Shading Conditions

Of all the renewable energy sources, solar photovoltaic (PV) power is considered to be a popular source owing to several advantages such as its free availability, absence of rotating parts, integration to building such as roof tops and less maintenance cost. The nonlinear current–voltage (I–V) characteristics and power generated from a PV array primarily depends on solar insolation/irradiation and panel temperature. The power output depends on the accuracy with which the nonlinear power–voltage (P–V) characteristics curve is traced by the maximum power point tracking (MPPT) controller. A DC-DC converter is commonly used in PV systems as an interface between the PV panel and the load, allowing the follow-up of the maximum power point (MPP). The objective of an efficient MPPT controller is to meet the following characteristics such as accuracy, robustness and faster tracking speed under partial shading conditions (PSCs) and climatic variations. To realize these objectives, numerous traditional techniques to artificial intelligence and bio-inspired techniques/algorithms have been recommended. Each technique has its own advantage and disadvantage. In view of that, in this thesis, a bio-inspired roach infestation optimization (RIO) algorithm is proposed to extract the maximum power from the PV system (PVS). In addition, the mathematical formulations and operation of the boost converter is investigated. To validate the effectiveness of the proposed RIO MPPT algorithm, MATLAB/Simulink simulations are carried out under varying environmental conditions, for example step changes in solar irradiance, and partial shading of the PV array. The obtained results are examined and compared with the particle swam optimization (PSO). The results demonstrated that the RIO MPPT performs remarkably in tracking with high accuracy as PSO based MPPT. Last but not the least, I am very grateful to the Arctic Centre for Sustainable Energy (ARC), UiT The Arctic University of Norway, Norway for providing an environment to d

Termites (Isoptera) in the Azores: an overview of the four invasive species currently present in the archipelago

In this contribution we summarize the current status of the known termites of the Azores (North Atlantic; 37-40° N, 25-31° W). Since 2000, four species of termites have been identified in the Azorean archipelago. These are spreading throughout the islands and becoming common structural and agricultural pests. Two termites of the Kalotermitidae family, Cryptotermes brevis (Walker) and Kalotermes flavicollis (Fabricius) are found on six and three of the islands, respectively. The other two species, the subterranean termites Reticulitermes grassei Clemént and R. flavipes (Kollar) of the Rhinotermitidae family are found only in confined areas of the cities of Horta (Faial) and Praia da Vitória (Terceira) respectively. Due to its location and weather conditions the Azorean archipelago is vulnerable to colonization by invasive species. The fact that there are four different species of termites in the Azores, all of them considered pests, is a matter of concern. Here we present a comparative description of these species, their known distribution in the archipelago, which control measures are being used against them, and what can be done in the future to eradicate and control these pests in the Azores

Improved roach-based algorithms for global optimization problems.

Ph. D. University of KwaZulu-Natal, Durban 2014.Optimization of systems plays an important role in various fields including mathematics, economics, engineering and life sciences. A lot of real world optimization problems exist across field of endeavours such as engineering design, space planning, networking, data analysis, logistic management, financial planning, risk management, and a host of others. These problems are constantly increasing in size and complexity, necessitating the need for improved techniques. Many conventional approaches have failed to solve complex problems effectively due to increasingly large solution space. This has led to the development of evolutionary algorithms that draw inspiration from the process of natural evolution. It is believed that nature provides inspirations that can lead to innovative models or techniques for solving complex optimization problems. Among the class of paradigm based on this inspiration is Swarm Intelligence (SI). SI is one of the recent developments of evolutionary computation. A SI paradigm is comprised of algorithms inspired by the social behaviour of animals and insects. SI-based algorithms have attracted interest, gained popularity and attention because of their flexibility and versatility. SIbased algorithms have been found to be efficient in solving real world optimization problems. Examples of SI algorithms include Ant Colony Optimization (ACO) inspired by the pheromone trail-following behaviour of ant species; Particle Swarm Optimization (PSO) inspired by flocking and swarming behaviour of insects and animals; and Bee Colony Optimization (BCO) inspired by bees’ food foraging. Recent emerging techniques in SI includes Roach-based Algorithms (RBA) motivated by cockroaches social behaviour. Two recently introduced RBA algorithms are Roach Infestation Optimization (RIO) and Cockroach Swarm Optimization (CSO) which have been applied to some optimization problems to achieve competitive results when compared to PSO. This study is motivated by the promising results of RBA, which have shown that the algorithms have potentials to be efficient tools for solving optimization problems. Extensive studies of existing RBA were carried out in this work revealing the shortcomings such as slow convergence and entrapment in local minima. The aim of this study is to overcome the identified drawbacks. We investigate RBA variants that are introduced in this work by introducing parameters such as constriction factor and sigmoid function that have proved effective for similar evolutionary algorithms in the literature. In addition components such as vigilance, cannibalism and hunger are incorporated into existing RBAs. These components are constructed by the use of some known techniques such as simple Euler, partial differential equation, crossover and mutation methods to speed up convergence and enhance the stability, exploitation and exploration of RBA. Specifically, a stochastic constriction factor was introduced to the existing CSO algorithm to improve its performance and enhance its ability to solve optimization problems involving thousands of variables. A CSO algorithm that was originally designed with three components namely chase-swarming, dispersion and ruthlessness is extended in this work with hunger component to improve its searching ability and diversity. Also, predator-prey evolution using crossover and mutation techniques were introduced into the CSO algorithm to create an adaptive search in each iteration thereby making the algorithm more efficient. In creating a discrete version of a CSO algorithm that can be used to evaluate optimization problems with any discrete range value, we introduced the sigmoid function. Furthermore, a dynamic step-size adaptation with simple Euler method was introduced to the existing RIO algorithm enhancing swarm stability and improving local and global searching abilities. The existing RIO model was also re-designed with the inclusion of vigilance and cannibalism components. The improved RBA were tested on established global optimization benchmark problems and results obtained compared with those from the literature. The improved RBA introduced in this work show better improvements over existing ones

Stochastic Constriction Cockroach Swarm Optimization for Multidimensional Space Function Problems

The effect of stochastic constriction on cockroach swarm optimization (CSO) algorithm performance was examined in this paper. A stochastic constriction cockroach swarm optimization (SCCSO) algorithm is proposed. A stochastic constriction factor is introduced into CSO algorithm for swarm stability enhancement; control cockroach movement from one position to another while searching for solution to avoid explosion; enhanced local and global searching capabilities. SCCSO performance was tested through simulation studies and its performance on multidimensional functions is compared with that of original CSO, modified cockroach swarm optimization (MCSO), and one of the well-known global optimization techniques in the literature known as line search restart techniques (LSRS). Standard benchmarks that have been widely used for global optimization problems are considered for evaluating the proposed algorithm. The selected benchmarks were solved up to 3000 dimensions by the proposed algorithm