scholarly journals The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
S. Salcedo-Sanz ◽  
J. Del Ser ◽  
I. Landa-Torres ◽  
S. Gil-López ◽  
J. A. Portilla-Figueras
Keyword(s):  
Coral Reefs ◽  
Wind Farm ◽  
Optimization Problems ◽  
Mobile Network ◽  
Benchmark Problems ◽  
Wind Farm Design ◽  
Coral Reefs Optimization Algorithm ◽  
Open Line ◽  
Complex Optimization ◽  
Real World Problems

This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems.

Offshore wind farm design with the Coral Reefs Optimization algorithm

2014 ◽  
Vol 63 ◽  
pp. 109-115 ◽  
Author(s):  
S. Salcedo-Sanz ◽  
D. Gallo-Marazuela ◽  
A. Pastor-Sánchez ◽  
L. Carro-Calvo ◽  
A. Portilla-Figueras ◽  
...  
Keyword(s):  
Coral Reefs ◽  
Optimization Algorithm ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Wind Farm Design ◽  
Coral Reefs Optimization Algorithm ◽  
Farm Design

AEGA: A New Real-Coded Genetic AlgorithmTaking Account of Extrapolation

2016 ◽  
Vol 20 (3) ◽  
pp. 429-437 ◽  
Author(s):  
Kento Uemura ◽  
◽  
Isao Ono
Keyword(s):  
Real World ◽  
Optimization Problems ◽  
Function Optimization ◽  
Benchmark Problems ◽  
Lens Design ◽  
Real World Problem ◽  
Real Coded Genetic Algorithm ◽  
Stable Manner ◽  
Ridge Structures ◽  
Real World Problems

This study proposes a new real-coded genetic algorithm (RCGA) taking account of extrapolation, which we call adaptive extrapolation RCGA (AEGA). Real-world problems are often formulated as black-box function optimization problems and sometimes have ridge structures and implicit active constraints. mAREX/JGG is one of the most powerful RCGAs that performs well against these problems. However, mAREX/JGG has a problem of search inefficiency. To overcome this problem, we propose AEGA that generates offspring outside the current population in a more stable manner than mAREX/JGG. Moreover, AEGA adapts the width of the offspring distribution automatically to improve its search efficiency. We evaluate the performance of AEGA using benchmark problems and show that AEGA finds the optimum with fewer evaluations than mAREX/JGG with a maximum reduction ratio of 45%. Furthermore, we apply AEGA to a lens design problem that is known as a difficult real-world problem and show that AEGA reaches the known best solution with approximately 25% fewer evaluations than mAREX/JGG.

scholarly journals Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm

2015 ◽  
Vol 3 (1) ◽  
pp. 24-36 ◽  
Author(s):  
Maziar Yazdani ◽  
Fariborz Jolai
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Population Based ◽  
Metaheuristic Algorithms ◽  
Benchmark Problems ◽  
The Past ◽  
Complex Optimization ◽  
Lion Optimization Algorithm ◽  
Population Based Algorithm

Abstract During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. In this paper, a new population based algorithm, the Lion Optimization Algorithm (LOA), is introduced. Special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm. Some benchmark problems are selected from the literature, and the solution of the proposed algorithm has been compared with those of some well-known and newest meta-heuristics for these problems. The obtained results confirm the high performance of the proposed algorithm in comparison to the other algorithms used in this paper.

scholarly journals Improving the Computational Efficiency for Optimization of Offshore Wind Turbine Jacket Substructure by Hybrid Algorithms

2020 ◽  
Vol 8 (8) ◽  
pp. 548
Author(s):  
Ding-Peng Liu ◽  
Tsung-Yueh Lin ◽  
Hsin-Haou Huang
Keyword(s):  
Optimization Problems ◽  
Hybrid Algorithms ◽  
Pattern Search ◽  
Offshore Wind ◽  
Benchmark Problems ◽  
Offshore Wind Turbine ◽  
Stochastic Algorithms ◽  
Swarm Optimization ◽  
Targeted Mutation ◽  
Real World Problems

When solving real-world problems with complex simulations, utilizing stochastic algorithms integrated with a simulation model appears inefficient. In this study, we compare several hybrid algorithms for optimizing an offshore jacket substructure (JSS). Moreover, we propose a novel hybrid algorithm called the divisional model genetic algorithm (DMGA) to improve efficiency. By adding different methods, namely particle swarm optimization (PSO), pattern search (PS) and targeted mutation (TM) in three subpopulations to become “divisions,” each division has unique functionalities. With the collaboration of these three divisions, this method is considerably more efficient in solving multiple benchmark problems compared with other hybrid algorithms. These results reveal the superiority of DMGA in solving structural optimization problems.

A Database Grid Service with a Novel Mutation Operator

2014 ◽  
Vol 989-994 ◽  
pp. 4869-4872
Author(s):  
Jian Cao ◽  
Yan Bin Li ◽  
Cong Yan
Keyword(s):  
Optimization Problems ◽  
Novel Mutation ◽  
Experimental Studies ◽  
Heterogeneous Databases ◽  
Grid Service ◽  
Benchmark Problems ◽  
Mutation Operator ◽  
Grid Portal ◽  
Complex Optimization ◽  
Improved Pso

Database grid service provides users with a unified interface to access to distributed heterogeneous databases resources. To overcome the weakness of collaborative services ability in different grid portal, a new grid portal architecture based on CSGPA (Collaborative Services Grid Portal Architecture), is proposed. This paper aims to enhance the performance of PSO in complex optimization problems and proposes an improved PSO variant by incorporating a novel mutation operator. Experimental studies on some well-known benchmark problems show that our approach achieves promising results.

A Novel Nature-Inspired Technique Based on Mushroom Reproduction for Constraint Solving and Optimization

2020 ◽  
Vol 19 (02) ◽  
pp. 2050010
Author(s):  
Mahdi Bidar ◽  
Malek Mouhoub ◽  
Samira Sadaoui ◽  
Hamidreza Rashidy Kanan
Keyword(s):  
High Performance ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Constraint Optimization ◽  
Growth Mechanisms ◽  
Natural Reproduction ◽  
Complex Optimization ◽  
Good Living ◽  
Constraint Optimization Problems ◽  
Real World Problems

Constraint optimization consists of looking for an optimal solution maximizing a given objective function while meeting a set of constraints. In this study, we propose a new algorithm based on mushroom reproduction for solving constraint optimization problems. Our algorithm, that we call Mushroom Reproduction Optimization (MRO), is inspired by the natural reproduction and growth mechanisms of mushrooms. This process includes the discovery of rich areas with good living conditions allowing spores to grow and develop their own colonies. Given that constraint optimization problems often suffer from a high-time computation cost, we thoroughly assess MRO performance on well-known constrained engineering and real-world problems. The experimental results confirm the high performance of MRO, comparing to other known metaheursitcs, in dealing with complex optimization problems.

scholarly journals Comparison between genetic algorithm and prey-predator algorithm

2014 ◽  
Vol 9 (4) ◽  
Author(s):  
Surafel Luleseged Tilahun ◽  
Hong Choon Ong
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Metaheuristic Algorithms ◽  
Benchmark Problems ◽  
Test Problems ◽  
Search Mechanism ◽  
Complex Optimization ◽  
Better Than

Metaheuristic algorithms are useful in solving complex optimization problems. Genetic algorithm (GA) is one of the well known and oldest metaheuristic algorithms. It was introduced in 1975 and has been used in many applications varying from engineering to management and many other fields as well. However, Prey-Predator algorithm (PPA) is one of recently introduced algorithm, in 2012, inspired by the interaction between preys and their predator. The motivation and the search mechanism for these two algorithms are different. In this paper the comparison of these two algorithms both from theoretical aspects and using simulation on selected benchmark problems is presented. According to the results, PPA performs better than GA in the selected test problems.

scholarly journals Constraint Consensus Based Artificial Bee Colony Algorithm for Constrained Optimization Problems

2019 ◽  
Vol 2019 ◽  
pp. 1-24 ◽  
Author(s):  
Liling Sun ◽  
Yuhan Wu ◽  
Xiaodan Liang ◽  
Maowei He ◽  
Hanning Chen
Keyword(s):  
Constrained Optimization ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Constrained Optimization Problems ◽  
Constrained Problems ◽  
Bee Colony ◽  
Hybrid Heuristic Algorithm ◽  
Complex Optimization ◽  
Constraint Consensus

Over the last few decades, evolutionary algorithms (EAs) have been widely adopted to solve complex optimization problems. However, EAs are powerless to challenge the constrained optimization problems (COPs) because they do not directly act to reduce constraint violations of constrained problems. In this paper, the robustly global optimization advantage of artificial bee colony (ABC) algorithm and the stably minor calculation characteristic of constraint consensus (CC) strategy for COPs are integrated into a novel hybrid heuristic algorithm, named ABCCC. CC strategy is fairly effective to rapidly reduce the constraint violations during the evolutionary search process. The performance of the proposed ABCCC is verified by a set of constrained benchmark problems comparing with two state-of-the-art CC-based EAs, including particle swarm optimization based on CC (PSOCC) and differential evolution based on CC (DECC). Experimental results demonstrate the promising performance of the proposed algorithm, in terms of both optimization quality and convergence speed.

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