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This paper provides an overview on a new evolutionary approach based on an intelligent multi-agent architecture to design Beta fuzzy systems (BFSs). The Methodology consists of two processes, a learning process using a clustering technique for the automated design of an initial Beta fuzzy system, and a multi-agent tuning process based on Particle Swarm Optimization algorithm to deal with the optimization...
The theoretical analysis of evolutionary algorithms is believed to be very important for understanding their internal search mechanism and thus to develop more efficient algorithms. This paper presents a simple mathematical analysis of the explorative search behavior of a recently developed metaheuristic algorithm called harmony search (HS). HS is a derivative-free real parameter optimization algorithm,...
Concentric Circular Antenna Array (CCAA) has several interesting features that makes it indispensable in mobile and communication applications. Here we have considered a uniform arrangement of elements where the inter-element spacing is kept half a wavelength. The main aim is to reduce the sidelobe levels and the primary lobe beamwidth as much as possible. Central to our design is a hybridization...
Nature Inspired Optimization Algorithms (NIOA) are inspired by biological and sociological phenomena and can take care of optimality on rough, discontinuous and multimodal surfaces. During the last few decades, these algorithms have been successfully applied for solving numerical bench mark problems and real life problems. This paper presents the application of two popular NIOA, namely Particle Swarm...
An Optimal Control is a set of differential equations describing the path of the control variables that minimize the cost functional (function of both state and control variables). Direct solution methods for optimal control problems treat them from the perspective of global optimization: perform a global search for the control function that optimizes the required objective. Invasive Weed Optimization...
Computer security is very important in these days. Computers are used probably in any industry and their protection against attacks is very important task. The protection usually consist in several levels. The first level is preventions. Intrusion detection system (IDS) may be used as next level. IDS is useful in detection of intrusions, but also in monitoring of security issues and the traffic. This...
In this article we describe an optimization-based design method for non-uniform, planar, and circular antenna arrays with the objective of achieving minimum side lobe levels for a specific first null beamwidth and also a minimum size of the circumference. Central to our design is a hybridization of two prominent metaheuristics of current interest namely the Invasive Weed Optimization (IWO) and the...
Several variants of the Particle Swarm Optimization (PSO) algorithm have been proposed in recent past to tackle the multi-objective optimization problems based on the concept of Pareto optimality. Although a plethora of significant research articles have so far been published on analysis of the stability and convergence properties of PSO as a single-objective optimizer, till date, to the best of our...
Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. However FCM is sensitive to initialization and is easily trapped in local optima. Particle swarm optimization (PSO) is a stochastic global...
The K-Modes algorithm is one of the most popular clustering algorithms in dealing with categorical data. But the random selection of starting centers in this algorithm may lead to different clustering results and falling into local optima. In this paper we proposed a swarm-based K-Modes algorithm. The experimental results over two well known Soybean and Congressional voting categorical data sets show...
Evolutionary Algorithms are inspired by biological and sociological motivations and can take care of optimality on rough, discontinuous and multimodal surfaces. During the last few decades, these algorithms have been successfully applied for solving numerical bench mark problems and real life problems. This paper presents the application of two popular Evolutionary Algorithms (EA); namely Particle...
Linear antenna array design is one of the most important electromagnetic optimization problems of current interest. This article describes the application of a recently developed metaheuristic algorithm, known as the invasive weed optimization (IWO), to optimize the spacing between the elements of the linear array to produce a radiation pattern with minimum side lobe level and null placement control...
In this study we discuss a method for evolution of quasigroups with desired properties based on genetic algorithms. Quasigroups are a well-known combinatorial design equivalent to the more familiar Latin squares. One of their most important properties is that all possible elements of certain quasigroup occur with equal probability. The quasigroups are evolved within a framework of a simple hash function...
Invasive weed optimization (IWO) has been found to be a simple but powerful algorithm for function optimization over continuous spaces. It has reportedly outperformed many types of evolutionary algorithms and other search heuristics when tested over both benchmark and real-world problems. However the performance of most search heuristics deteriorates severely when applied to the task of optimization...
This paper presents a new diversity guided particle swarm optimization algorithm (PSO) named beta mutation PSO or BMPSO for solving global optimization problems. The BMPSO algorithm makes use of an evolutionary programming based mutation operator to maintain the level of diversity in the swarm population, thereby maintaining a good balance between the exploration and exploitation phenomena and preventing...
The bacterial foraging optimization (BFO) algorithm is a nature and biologically inspired computing method. We propose an alternative solution integrating bacterial foraging optimization algorithm and tabu search (TS) algorithm namely TS-BFO. We modify the original BFO via established a self-control multi-length chemotactic step mechanism, and introduce rao metric. We utilize it to solve motif discovery...
Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing systems and it is also an appealing NP-complete problem. There is a number of heuristic and meta-heuristic algorithms that were tailored to deal with scheduling of independent jobs. In this paper we investigate the efficiency of differential evolution on the scheduling problem.
Particle Swarm Optimization (PSO) is arguably one of the most popular nature- inspired algorithms for real parameter optimization at present. The existing theoretical research on PSO is mostly based on the gbest (global best) particle topology, which usually is susceptible to false or premature convergence over multi-modal fitness landscapes. The present standard PSO (SPSO 2007) uses an lbest (local...
Since their appearance in 1993, first approaching the Shannon limit, turbo codes gave a new direction in the channel encoding field, especially since they have been adopted for multiple telecommunication norms. To obtain good performance, it is necessary to design a robust turbo code interleaver. This paper proposes a a differential evolution approach to find above average turbo code interleavers...
This paper presents a novel optimization approach using improved harmony search (IHS) algorithm to solve economic power dispatch problem. The proposed methodology easily takes care of different equality and inequality constraints of the power dispatch problem to find the optimal solution. To show its efficiency, the proposed algorithm is applied to single area and multi area system of four area having...
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