In this paper a CAD (Computer Aided Diagnosis) system is proposed to optimize the feature set using hybrid of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) technique called Genetical Swarm Optimization (GSO) in Digital Mammogram. Even though PSO is a good optimization technique, it may be trapped in local minima and may prematurely converge. So, the genetic operators are used in ...

Wireless Sensor Networks (WSNs) consists of arbitrarily placed tiny sensors which monitor the target field. Every location of the target field is said to be with at least one sensor. The sensors must be deployed in such a way that every sensor is efficiently used to monitor the target field with less coverage loss. Glow worm Swarm Optimization (GSO) technique is swarm intelligence based techniq...

In this paper, two new cluster analysis methods based on glowworm swarm optimization (GSO) algorithm are proposed. The first algorithm showed how GSO can be used to self-organization cluster analysis. The second algorithm is hybrid the GSO clustering analysis with the K-means algorithm to accelerate classification. Two clustering algorithms are tested on three data sets; experimental results sh...

The main purpose of this work is the comparison of several objective functions for optimization of the vertical alignment. To this end, after formulation of optimum vertical alignment problem based on different constraints, the objective function was considered as four forms including: 1) the sum of the absolute value of variance between the vertical alignment and the existing ground; 2) the su...

In this paper we propose a new distributed double guided hybrid algorithm combining the particle swarm optimization (PSO) with genetic algorithms (GA) to resolve maximal constraint satisfaction problems (Max-CSPs). It consists on a multi-agent approach inspired by a centralized version of hybrid algorithm called Genetical Swarm Optimization (GSO). Our approach consists of a set of evolutionary ...

This paper presents multimodal function optimization, using a nature-inspired glowworm swarm optimization (GSO) algorithm, with applications to collective robotics. GSO is similar to ACO and PSO but with important differences. A key feature of the algorithm is the use of an adaptive local-decision domain, which is used effectively to detect the multiple optimum locations of the multimodal funct...

Adaptability becomes important in developing metaheuristic algorithms, especially tackling stagnation. Unfortunately, almost all metaheuristics are not equipped with an adaptive approach that makes them change their strategy when stagnation happens during iteration. Based on this consideration, a new metaheuristic, called balance optimizer (ABO), is proposed paper. ABO's unique focuses exploita...

in today’s world renewable energy is highly demanding because of increasing oil and fuelprices, desire and necessity to avert irreversible climate damage and also the unreliability of the non -renewablesources. in renewable energy, water is an important source. previously water is used only for survivalof human being as much as food but no attentionhas been givenen to economical use and conserv...

Actually, path planning is one of the most crucial aspects mobile robots study. The primary goal this research to develop a solution issues that occur when “mobile robot” operates in static environment. problem handled by finding collision-free meets given criteria for shortest distance with quite smoothness path. Two nature-inspired metaheuristic algorithms are used computation. By leading hyb...

In this chapter we present the design and optimization of GPU implementations of two popular chemical similarity techniques: Gaussian shape overlay (GSO) and LINGO. GSO involves a data-parallel, arithmetically intensive iterative numerical optimization; we use it to examine issues of thread parallelism, arithmetic optimization, and CPU-GPU transfer overhead minimization. LINGO is a string simil...