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A coral reefs optimization algorithm with substrate layer for robust Wi-Fi channel assignment

SOFT COMPUTING 2019年第23期23卷 12621-12640页

作者： Camacho-Gomez, Carlos Marsa-Maestre, Ivan Manuel Gimenez-Guzman, Jose Salcedo-Sanz, Sancho Univ Alcala Dept Signal Proc & Commun Madrid Spain Univ Alcala Comp Engn Dept Madrid Spain

In this paper, we tackle a problem of frequency assignment in Wi-Fi networks with a novel evolutionary-type algorithm. In this version of the problem, we consider the interferences originated by the access points, and also by the clients and all the 11 available channels in the 2.4 GHz Wi-Fi frequency band. The proposed evolutionary-type algorithm is the coral reefs optimization approach with substrate layer (CRO-SL). It is a recently proposed algorithm, which simulates the processes which occur in real coral reefs, including the reproduction and fight for the space of living corals. This version of the algorithm includes a layer of "substrates" which allows using different search patterns jointly in the algorithm. This way, the CRO-SL is able to apply search patterns such as harmony search, differential evolution, Gaussian-based mutations and other traditional and novel search procedures, including local search algorithms, within a single population of solutions. We show the good performance of the proposed approach in a real case study of Wi-Fi frequency assignment, in the Polytechnic School building of the Universidad de Alcala (Spain), where different realistic scenarios of the problem have been simulated and successfully solved with the CRO-SL algorithm.

关键词： Wi-Fi channel assignment Graph coloring coral reefs optimization algorithm Meta-heuristics

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A novel coral reefs optimization algorithm for materialized view selection in data warehouse environments

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APPLIED INTELLIGENCE 2019年第11期49卷 3965-3989页

作者： Azgomi, Hossein Sohrabi, Mohammad Karim Islamic Azad Univ Rasht Branch Young Researchers & Elite Club Rasht Iran Islamic Azad Univ Semnan Branch Dept Comp Engn Semnan Iran

High response time of analytical queries is one of the most challenging issues of data warehouses. Complicated nature of analytical queries and enormous volume of data are the most important reasons of this high response time. The aim of materialized view selection is to reduce the response time of these analytical queries. For this purpose, the search space is firstly constructed by producing the set of all possible views based on given queries and then, the (semi-) optimal set of materialized views will be selected so that the queries can be answered at the lowest cost using them. Various materialized view selection methods have been proposed in the literature, most of which are randomized methods due to the time-consuming nature of this problem. Randomized view selection methods choose a semi-optimal set of proper views for materialization in an appropriate time using one or a combination of some meta-heuristic(s). In this paper, a novel coral reefs optimization-based method is introduced for materialized view selection in a data warehouse. coral reefs optimization algorithm is an optimization method that solves problems by simulating the coral behaviors for placement and growth in reefs. In the proposed method, each solution of the problem is considered as a coral, which is always trying to be placed and grow in the reefs. In each step, special operators of the coral reefs optimization algorithm are applied on the solutions. After several steps, better solutions are more likely to survive and grow on the reefs. The best solution is finally chosen as the final solution of the problem. The practical evaluations of the proposed method show that this method offers higher quality solutions than other similar random methods in terms of coverage rate of queries.

关键词： Materialized view selection coral reefs optimization algorithm Data warehouse Multiple view processing plan Randomized algorithms

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Offshore wind farm design with the coral reefs optimization algorithm

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RENEWABLE ENERGY 2014年 63卷 109-115页

作者： Salcedo-Sanz, S. Gallo-Marazuela, D. Pastor-Sanchez, A. Carro-Calvo, L. Portilla-Figueras, A. Prieto, L. Univ Alcala de Henares Dept Signal Proc & Commun Madrid 28871 Spain Dept Energy Resource Iberdrola Spain

This paper presents a novel algorithm for wind farm design and layout optimization: the coral reefs optimization algorithm (CRO). The CRO is a novel bio-inspired approach, based on the simulation of reef formation and coral reproduction. The CRO is fully described and detailed in this paper, and then applied to the design of a real offshore wind farm in northern Europe. It is shown that the CRO outperforms the results of alternative algorithms in this problem, such as Evolutionary Approaches, Differential Evolution or Harmony Search algorithms. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Offshore wind farm Design and optimization coral reefs optimization algorithm

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A novel coral reefs optimization algorithm with substrate layers for optimal battery scheduling optimization in micro-grids

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SOFT COMPUTING 2016年第11期20卷 4287-4300页

作者： Salcedo-Sanz, S. Camacho-Gomez, C. Mallol-Poyato, R. Jimenez-Fernandez, S. Del Ser, J. Univ Alcala Dept Signal Proc & Commun Madrid Spain TECNALIA Optima Grp Derio 48160 Bizkaia Spain Univ Basque Country EHU UPV Dept Commun Engn Bilbao 48013 Bizkaia Spain

In this paper we propose a coral reefs optimization algorithm with substrate layers (CRO-SL) to tackle the battery scheduling optimization problem in micro-grids (MGs). Specifically, we consider a MG that includes renewable generation and different loads, defined by their power profiles, and is equipped with an energy storage device (battery) to address its scheduling (charge/discharge duration and occurrence) in a real scenario of variable electricity prices. The CRO-SL is a recently proposed meta-heuristic which promotes co-evolution of different exploration models within a unique population. We fully describe the proposed CRO-SL algorithm, including its initialization and the different operators implemented in the algorithm. Experiments in a real MG scenario are carried out. To show the good battery scheduling performance of the proposed CRO-SL, we have compared the results with what we called a deterministic procedure. The deterministic charge/discharge approach is defined as a fixed way of using the energy storage device that only depends on the pattern of the loads and generation profiles considered. Hourly values of both generation and consumption profiles have been considered, and the good performance of the proposed CRO-SL is shown for four different weeks of the year (one per season), where the effect of the battery scheduling optimization obtains savings up 10 % of the total electricity cost in the MG, when compared with the deterministic procedure.

关键词： Micro-grid Battery scheduling optimization coral reefs optimization algorithm Meta-heuristics

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A coral reefs optimization algorithm for optimal mobile network deployment with electromagnetic pollution control criterion

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APPLIED SOFT COMPUTING 2014年 24卷 239-248页

作者： Salcedo-Sanz, S. Garcia-Diaz, P. Portilla-Figueras, J. A. Del Ser, J. Gil-Lopez, S. Univ Alcala Dept Signal Proc & Commun Madrid 28871 Spain Tecnalia Res & Innovat Zamudio Bizkaia Spain

In this paper we apply a novel meta-heuristic approach, the coral reefs optimization (CRO) algorithm, to solve a Mobile Network Deployment Problem (MNDP), in which the control of the electromagnetic pollution plays an important role. The CRO is a new bio-inspired meta-heuristic algorithm based on the growing and evolution of coral reefs. The aim of this paper is therefore twofold: first of all, we study the performance of the CRO approach in a real hard optimization problem, and second, we solve an important problem in the field of telecommunications, including the minimization of electromagnetic pollution as a key concept in the problem. We show that the CRO is able to obtain excellent solutions to the MNDP in a real instance in Alcala de Henares (Madrid, Spain), improving the results obtained by alternative algorithms such as Evolutionary, Particle Swarm optimization or Harmony Search algorithms. (C) 2014 Elsevier B.V. All rights reserved.

关键词： coral reefs optimization algorithm Mobile network deployment Network optimization algorithms

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A review on the coral reefs optimization algorithm: new development lines and current applications

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PROGRESS IN ARTIFICIAL INTELLIGENCE 2017年第1期6卷 1-15页

作者： Salcedo-Sanz, S. Univ Alcala Escuela Politecn Super Dept Signal Proc & Commun Madrid 28871 Spain

The simulation of biological processes has produced some of themost important meta-heuristics algorithms for optimization. Evolutionary algorithms were the first, and probably the most applied, algorithms coming from biological inspiration, but there have been many more, specially in the last few years. This paper describes a special class of evolutionary algorithms recently proposed, the coral reefs optimization algorithm (CRO), which simulates some specific biological processes that occur in real coral reefs. The simulation of these processes leads to an evolutionary algorithm in which similarities with Simulated Annealing have been introduced. Moreover, the inclusion of alternative processes occurring in coral reefs produces very effective co-evolution versions of the CRO algorithm, specially well suited for optimization problems with inherent variable length encodings, or able to co-evolve several exploration patterns within the same population. All these issues related to the CRO approach are thoroughly described in the paper, and also a fully description of the main applications of the algorithm in engineering optimization problems is given to close this first review on the CRO.

关键词： coral reefs optimization algorithm Meta-heuristics Bio-inspired algorithms Applications

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Cloud Computing Task Scheduling Method Based on a coral reefs optimization algorithm 25

Cloud Computing Task Scheduling Method Based on a Coral Reef...

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25th IEEE International Conference on Parallel and Distributed Systems (IEEE ICPADS)

作者： Xu, Hongpo Chen, Wei Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing Peoples R China

ISBN: (纸本)9781728125831

Task scheduling is a difficult non-deterministic polynomial problem. optimization of the scheduling algorithm is the key to improve the efficiency of cloud computing. The traditional meta-heuristic algorithm has slow convergence rate and is easy to fall into local optimal value. This paper proposes a new scheduling method based on a coral reefs algorithm. Firstly, the task scheduling model is formally described. The objective function is proposed to calculate load balancing rate, resource utilization and load balancing stability. Then the representation method of coral reef and the coding scheme of polyps are designed. Matrix random mapping method is applied to improve the variation effect of polyps. Finally, Ant Colony optimization(ACO), the Genetic algorithm(GA) and Round Robin(RR) algorithms are compared in terms of completion time, convergence effect and resource load. The simulation results show that the coral reef algorithm has reduced the completion time by 6.4%, 25.1%, 51.3%, and increased resource utilization by 10.0%, 15.2% and 51.3% when it is compared with the other three algorithms. It shows that the coral reef algorithm is suitable for task scheduling in the cloud environment.

关键词： Task scheduling Load balancing coral reefs optimization algorithm Cloud computing

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Effective multi-objective optimization with the coral reefs optimization algorithm

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ENGINEERING optimization 2016年第6期48卷 966-984页

作者： Salcedo-Sanz, S. Pastor-Sanchez, A. Portilla-Figueras, J. A. Prieto, L. Univ Alcala Escuela Politecn Super Dept Signal Proc & Commun Madrid Spain Iberdrola Dept Energy Resource Madrid Spain

In this article a new algorithm for multi-objective optimization is presented, the Multi-Objective coral reefs optimization (MO-CRO) algorithm. The algorithm is based on the simulation of processes in coral reefs, such as corals' reproduction and fight for space in the reef. The adaptation to multi-objective problems is a process based on domination or non-domination during the process of fight for space in the reef. The final MO-CRO is an easily-implemented and fast algorithm, simple and robust, since it is able to keep diversity in the population of corals (solutions) in a natural way. The experimental evaluation of this new approach for multi-objective optimization problems is carried out on different multi-objective benchmark problems, where the MO-CRO has shown excellent performance in cases with limited computational resources, and in a real-world problem of wind speed prediction, where the MO-CRO algorithm is used to find the best set of features to predict the wind speed, taking into account two objective functions related to the performance of the prediction and the computation time of the regressor.

关键词： coral reefs optimization algorithm multi-objective optimization bio-inspired algorithms wind speed prediction extreme learning machines

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Optimal Generation Scheduling in Hydro-Power Plants with the coral reefs optimization algorithm

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ENERGIES 2021年第9期14卷

作者： Marcelino, Carolina Gil Camacho-Gomez, Carlos Jimenez-Fernandez, Silvia Salcedo-Sanz, Sancho Univ Alcala Dept Signal Proc & Commun Madrid 28805 Spain Univ Fed Rio de Janeiro Inst Comp BR-21941972 Rio De Janeiro Brazil Univ Politecn Madrid Dept Informat Syst Campus Sur Madrid 28031 Spain

Hydro-power plants are able to produce electrical energy in a sustainable way. A known format for producing energy is through generation scheduling, which is a task usually established as a Unit Commitment problem. The challenge in this process is to define the amount of energy that each turbine-generator needs to deliver to the plant, to fulfill the requested electrical dispatch commitment, while coping with the operational restrictions. An optimal generation scheduling for turbine-generators in hydro-power plants can offer a larger amount of energy to be generated with respect to non-optimized schedules, with significantly less water consumption. This work presents an efficient mathematical modelling for generation scheduling in a real hydro-power plant in Brazil. An optimization method based on different versions of the coral reefs optimization algorithm with Substrate Layers (CRO) is proposed as an effective method to tackle this problem. This approach uses different search operators in a single population to refine the search for an optimal scheduling for this problem. We have shown that the solution obtained with the CRO using Gaussian search in exploration is able to produce competitive solutions in terms of energy production. The results obtained show a huge savings of 13.98 billion (liters of water) monthly projected versus the non-optimized scheduling.

关键词： generation scheduling hydro-power plants coral reefs optimization algorithm meta-heuristics bio-inspired algorithms energy efficiency

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Cloud Computing Task Scheduling Method Based on a coral reefs optimization algorithm

Cloud Computing Task Scheduling Method Based on a Coral Reef...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Wei Chen hongpo xu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728125848

Task scheduling is a difficult non-deterministic polynomial problem. optimization of the scheduling algorithm is the key to improve the efficiency of cloud computing. The traditional meta-heuristic algorithm has slow convergence rate and is easy to fall into local optimal value. This paper proposes a new scheduling method based on a coral reefs algorithm. Firstly, the task scheduling model is formally described. The objective function is proposed to calculate load balancing rate, resource utilization and load balancing stability. Then the representation method of coral reef and the coding scheme of polyps are designed. Matrix random mapping method is applied to improve the variation effect of polyps. Finally, Ant Colony optimization(ACO), the Genetic algorithm(GA) andRound Robin(RR) algorithms are compared in terms of completion time, convergence effect and resource load. The simulation results show that the coral reef algorithm has reduced the completion time by 6.4%, 25.1%, 51.3%, and increased resource utilization by 10.0%, 15.2% and 51.3% when it is compared with the other three algorithms. It shows that the coral reef algorithm is suitable for task scheduling in the cloud environment.

关键词： Task scheduling Load balancing coral reefs optimization algorithm Cloud computing TASK SCHEDULING Cloud Computing Load balancing coral reef Resource utilization Completion time Ant colony optimization algorithms Polyps representation method labeling scheme

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