Multicore Parallelization of CHC for Optimal Aerogenerator Placement in Wind Farms

In this paper, we study a parallelization of CHC algorithm (Crossover elitism population, Half uniform crossover combination, Cataclysm mutation) to solve the problem of placement of wind turbines in a wind farm. We also analyze the solutions obtained when we use both, the sequential and parallel version for the CHC algorithm. In this case we study the behavior of parallel metaheuristics using an island model to distribute the algorithm in different cores and compare this proposal with the sequential version to analyse the number of evaluation to find the best configuration, output power extracted, plant coefficient, evaluations needed, memory consumption, and execution time for different number of core and different problem sizes.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

Multicore Parallelization of CHC for Optimal Aerogenerator Placement in Wind Farms

In this paper, we study a parallelization of CHC algorithm (Crossover elitism population, Half uniform crossover combination, Cataclysm mutation) to solve the problem of placement of wind turbines in a wind farm. We also analyze the solutions obtained when we use both, the sequential and parallel version for the CHC algorithm. In this case we study the behavior of parallel metaheuristics using an island model to distribute the algorithm in different cores and compare this proposal with the sequential version to analyse the number of evaluation to find the best configuration, output power extracted, plant coefficient, evaluations needed, memory consumption, and execution time for different number of core and different problem sizes.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

Multicore Parallelization of CHC for Optimal Aerogenerator Placement in Wind Farms

In this paper, we study a parallelization of CHC algorithm (Crossover elitism population, Half uniform crossover combination, Cataclysm mutation) to solve the problem of placement of wind turbines in a wind farm. We also analyze the solutions obtained when we use both, the sequential and parallel version for the CHC algorithm. In this case we study the behavior of parallel metaheuristics using an island model to distribute the algorithm in different cores and compare this proposal with the sequential version to analyse the number of evaluation to find the best configuration, output power extracted, plant coefficient, evaluations needed, memory consumption, and execution time for different number of core and different problem sizes.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

BAS-ADAM: an ADAM based approach to improve the performance of beetle antennae search optimizer

In this paper, we propose enhancements to Beetle Antennae search ( BAS ) algorithm, called BAS-ADAM, to smoothen the convergence behavior and avoid trapping in local-minima for a highly non-convex objective function. We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation ( ADAM ) update rule. The proposed algorithm also increases the convergence rate in a narrow valley. A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size. Since ADAM is traditionally used with gradient-based optimization algorithms, therefore we first propose a gradient estimation model without the need to differentiate the objective function. Resultantly, it demonstrates excellent performance and fast convergence rate in searching for the optimum of non-convex functions. The efficiency of the proposed algorithm was tested on three different benchmark problems, including the training of a high-dimensional neural network. The performance is compared with particle swarm optimizer ( PSO ) and the original BAS algorithm

GA and PSO applied towind energy optimization

In this article we analyze two kinds of metaheuristic algorithms applied to wind farm optimization. The basic idea is to utilize CHC (a sort of GA) and GPSO (a sort of PSO) algorithms to obtain an acceptable configuration of wind turbines in the wind farm that maximizes the total output energy and minimize the number of wind turbines used. The energy produced depends of the farm geometry, wind conditions and the terrain where it is settled. In this work we will analyze three study farm scenarios with different wind speeds and we will apply both algorithms to analyze the performance of the algorithms and the behavior of the computed wind farm designs.Presentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Benchmarking a wide spectrum of metaheuristic techniques for the radio network design problem

The radio network design (RND) is an NP-hard optimization problem which consists of the maximization of the coverage of a given area while minimizing the base station deployment. Solving RND problems efficiently is relevant to many fields of application and has a direct impact in the engineering, telecommunication, scientific, and industrial areas. Numerous works can be found in the literature dealing with the RND problem, although they all suffer from the same shortfall: a noncomparable efficiency. Therefore, the aim of this paper is twofold: first, to offer a reliable RND comparison base reference in order to cover a wide algorithmic spectrum, and, second, to offer a comprehensible insight into accurate comparisons of efficiency, reliability, and swiftness of the different techniques applied to solve the RND problem. In order to achieve the first aim we propose a canonical RND problem formulation driven by two main directives: technology independence and a normalized comparison criterion. Following this, we have included an exhaustive behavior comparison between 14 different techniques. Finally, this paper indicates algorithmic trends and different patterns that can be observed through this analysis.Publicad

Optimal Portfolio Management for Engineering Problems Using Nonconvex Cardinality Constraint: A Computing Perspective

The problem of portfolio management relates to the selection of optimal stocks, which results in a maximum return to the investor while minimizing the loss. Traditional approaches usually model the portfolio selection as a convex optimization problem and require the calculation of gradient. Note that gradient-based methods can stuck at local optimum for complex problems and the simplification of portfolio optimization to convex, and further solved using gradient-based methods, is at a high cost of solution accuracy. In this paper, we formulate a nonconvex model for the portfolio selection problem, which considers the transaction cost and cardinality constraint, thus better reflecting the decisive factor affecting the selection of portfolio in the real-world. Additionally, constraints are put into the objective function as penalty terms to enforce the restriction. Note that this reformulated problem cannot be readily solved by traditional methods based on gradient search due to its nonconvexity. Then, we apply the Beetle Antennae Search (BAS), a nature-inspired metaheuristic optimization algorithm capable of efficient global optimization, to solve the problem. We used a large real-world dataset containing historical stock prices to demonstrate the efficiency of the proposed algorithm in practical scenarios. Extensive experimental results are presented to further demonstrate the efficacy and scalability of the BAS algorithm. The comparative results are also performed using Particle Swarm Optimizer (PSO), Genetic Algorithm (GA), Pattern Search (PS), and gradient-based fmincon (interior-point search) as benchmarks. The comparison results show that the BAS algorithm is six times faster in the worst case (25 times in the best case) as compared to the rival algorithms while achieving the same level of performance

Revisiting the Evolution and Application of Assignment Problem: A Brief Overview

The assignment problem (AP) is incredibly challenging that can model many real-life problems. This paper provides a limited review of the recent developments that have appeared in the literature, meaning of assignment problem as well as solving techniques and will provide a review on   a lot of research studies on different types of assignment problem taking place in present day real life situation in order to capture the variations in different types of assignment techniques. Keywords: Assignment problem, Quadratic Assignment, Vehicle Routing, Exact Algorithm, Bound, Heuristic etc

Dominance-Based Multiobjective Simulated Annealing

Copyright © 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Simulated annealing is a provably convergent optimizer for single-objective problems. Previously proposed multiobjective extensions have mostly taken the form of a single-objective simulated annealer optimizing a composite function of the objectives. We propose a multiobjective simulated annealer utilizing the relative dominance of a solution as the system energy for optimization, eliminating problems associated with composite objective functions. We also propose a method for choosing perturbation scalings promoting search both towards and across the Pareto front. We illustrate the simulated annealer's performance on a suite of standard test problems and provide comparisons with another multiobjective simulated annealer and the NSGA-II genetic algorithm. The new simulated annealer is shown to promote rapid convergence to the true Pareto front with a good coverage of solutions across it comparing favorably with the other algorithms. An application of the simulated annealer to an industrial problem, the optimization of a code-division-multiple access (CDMA) mobile telecommunications network's air interface, is presented and the simulated annealer is shown to generate nondominated solutions with an even and dense coverage that outperforms single objective genetic algorithm optimizers

GA and PSO applied towind energy optimization

In this article we analyze two kinds of metaheuristic algorithms applied to wind farm optimization. The basic idea is to utilize CHC (a sort of GA) and GPSO (a sort of PSO) algorithms to obtain an acceptable configuration of wind turbines in the wind farm that maximizes the total output energy and minimize the number of wind turbines used. The energy produced depends of the farm geometry, wind conditions and the terrain where it is settled. In this work we will analyze three study farm scenarios with different wind speeds and we will apply both algorithms to analyze the performance of the algorithms and the behavior of the computed wind farm designs.Presentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI