Optimization algorithms

Teaching−Learning-Based Optimization Algorithm TLBO is a recently proposed meta-heuristic that imitates a successful and dynamic educational strategy in a classroom [29-31]. Similar to most evolutionary algorithms, TLBO is a population-based algorithm. The population consists of some students and a teacher. The teacher is the most knowledgeable one in the population. The main advantage of this algorithm over other evolutionary algorithms is that TLBO has no adjustable parameters, so there is no

Nature-Inspired Metaheuristic Optimization Algorithms-A Review Pragati Loomba Sonali Tiwari And Neerja Negi Student, Faculty of Computer Applications Assistant Professor, Faculty of Computer Applications Manav Rachna International University Manav Rachna International University Faridabad Faridabad loombapragati.pl@gmail.com sonali.fca@mriu.edu.in neerja.fca@mriu.edu.in Abstract - Now a day nature-inspired algorithms become a current trend and is applicable to almost every area. This paper

A Novel Study of Bee Colony Optimization Algorithms for their Applications into Various Domains INTRODUCTION The Bee Colony Optimization (BCO) is a technique based on the mutual understanding of the natural bees in food foraging process. It is a population based search algorithm for optimizing numerical problems. Bees are classic example of teamwork experience, coordination and synchronization. The way they work is remarkable. They carry out a kind of neighborhood search combined with global search

Data mining & Evolutionary algorithms for Multi-objective Optimization problems: A study. Data mining is the process of extracting the knowledge from the huge database available. The ultimate aim of data mining involves prediction based on the knowledge gained. Data mining is known as Knowledge Discovery in Databases (KDD) which is different ways mainly prediction and description. When data mining applied over the real time problem which puts us into trouble by having conflicting objectives to achieve

optimisation approach on requirements selection and optimisation is similar with search-based requirements optimisation. The only difference is that, instead of using search-based optimisation algorithm, the search-based requirements selection and optimisation problem is tracked with exact optimisation algorithm. There are three main categories exact optimisations found in the literature. They are Integer linear programming [25], dynamic programming [26], and exhaustive search [27]. Uncertainty is

[46] proposed a Tabu Search (TS) based memory-mechanism for problem solving. It was capable of navigating both local information and in-formation from search space. They took tabu tenure as the performance measuring metrics. The efficiency of this algorithm was verified by testing four benchmark problems using problem solving software. The results showed that TS produced globally optimal solution in CPU time less than 0.1 seconds. Further they have generated large random instances for testing and get

Optimization of Solvent Assisted SAGD using Hybrid Optimization Algorithms by Vinit Ganesh Menon Project Report Presented in Partial Fulfillment of the Requirements for the Degree of Master of Engineering University of Alberta November 2014 Abstract In recent years, several Steam Assisted Gravity Drainage (SAGD) projects have proven effective for the recovery of heavy oil and bitumen and solvent assisted projects have shown positive indication of improved performance. The important

A Hybrid Cuckoo Algorithm and Practice Swarm Optimization for Planning of Parking Lots in Distribution systems Mohamad Esmaili*, 1: Faculty of Engineering, Department of Electrical Engineering,Azad Shiraz University, Shiraz, Iran *Corresponding author. Tel +98-551-5824978; fax: +98-5007350826. E-mail addresses: mohamad709@gmail.com Abstract-Plug in Electric Vehicles (PEVs) have been proportionally innovated as a sufficient solution in smart grids in response to Green House Gases (GHGs)

sets are reevaluated using a new fitness function based on wrapper-based attribute reduction. The initial agents/solutions for this second stage of optimization are guaranteed to have minor correlation and hence are the candidate for further improvement towards the classification accuracy fitness function. So, the cuckoo search completes the rest of optimization steps with the initial

into 2-D search, the well-known subspace MUSIC algorithm is used for the examination of the received spatial information, and then it estimates each spatial spectrum in which the Azimuth Angle of Arrival (AOA) and Elevation Angle of Arrival (EOA) of all the paths at each URA WiFi access point is located. After that, because our system is considered under very low SNR, a set of spectra at some APs might be influenced, so, a fine-grained fusion algorithm has been added, it computes the minimum errors