scholarly journals Extended multi-objective firefly algorithm for hydropower energy generation

2017 ◽  
Vol 19 (5) ◽  
pp. 734-751 ◽  
Author(s):  
Omid Bozorg-Haddad ◽  
Irene Garousi-Nejad ◽  
Hugo A. Loáiciga
Keyword(s):  
Firefly Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Hydropower Generation ◽  
Local Optima ◽  
Multi Objective ◽  
Reservoir System ◽  
Study Results ◽  
Highly Nonlinear ◽  
Matlab Programming

Classical methods have severe limitations (such as being trapped in local optima, and the curse of dimensionality) to solve optimization problems. Evolutionary or meta-heuristic algorithms are currently favored as the tools of choice for tackling such complex non-linear reservoir operations. This paper evaluates the performance of an extended multi-objective developed firefly algorithm (MODFA). The MODFA script code was developed using the MATLAB programming language and was applied in MATLAB to optimize hydropower generation by a three-reservoir system in Iran. The two objectives used in the present study are the maximization of the reliability of hydropower generation and the minimization of the vulnerability to generation deficits of the three-reservoir system. Optimal Paretos (OPs) obtained with the MODFA are compared with those obtained with the multi-objective genetic algorithm (MOGA) and the multi-objective firefly algorithm (MOFA) for different levels of performance thresholds (50%, 75%, and 100%). The case study results demonstrate that the MODFA is superior to the MOGA and MOFA for calculating proper OPs with distinct solutions and a wide distribution of solutions. This study's results show that the MODFA solves multi-objective multi-reservoir operation system with the purpose of hydropower generation that are highly nonlinear that classical methods cannot solve.

A Hybrid Optimization Algorithm for Single and Multi-Objective Optimization Problems

2017 ◽  
pp. 491-521 ◽  
Author(s):  
Rizk M. Rizk-Allah ◽  
Aboul Ella Hassanien
Keyword(s):  
Optimization Algorithm ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Hybrid Optimization ◽  
Multi Objective Optimization ◽  
Local Optima ◽  
Fruit Fly Optimization ◽  
Multi Objective ◽  
Hybrid Optimization Algorithm

This chapter presents a hybrid optimization algorithm namely FOA-FA for solving single and multi-objective optimization problems. The proposed algorithm integrates the benefits of the fruit fly optimization algorithm (FOA) and the firefly algorithm (FA) to avoid the entrapment in the local optima and the premature convergence of the population. FOA operates in the direction of seeking the optimum solution while the firefly algorithm (FA) has been used to accelerate the optimum seeking process and speed up the convergence performance to the global solution. Further, the multi-objective optimization problem is scalarized to a single objective problem by weighting method, where the proposed algorithm is implemented to derive the non-inferior solutions that are in contrast to the optimal solution. Finally, the proposed FOA-FA algorithm is tested on different benchmark problems whether single or multi-objective aspects and two engineering applications. The numerical comparisons reveal the robustness and effectiveness of the proposed algorithm.

Optimization Design of Trusses Based on Covariance Matrix Adaptation Evolution Strategy Algorithm

2012 ◽  
Vol 215-216 ◽  
pp. 133-137
Author(s):  
Guo Shao Su ◽  
Yan Zhang ◽  
Zhen Xing Wu ◽  
Liu Bin Yan
Keyword(s):  
Stochastic Optimization ◽  
Covariance Matrix ◽  
Optimization Problems ◽  
Fitness Function ◽  
Optimization Methods ◽  
Evolution Strategy ◽  
Covariance Matrix Adaptation ◽  
Study Results ◽  
Highly Nonlinear ◽  
Adaptation Evolution

Covariance matrix adaptation evolution strategy algorithm (CMA-ES) is a newly evolution algorithm. It has become a powerful tool for solving highly nonlinear multi-peak optimization problems. In many real-world optimization problems, the location of multiple optima is often required in a search space. In order to evaluate the solution, thousands of fitness function evaluations are involved that is a time consuming or expensive processes. Therefore, conventional stochastic optimization methods meet a special challenge for a very large number of problem function evaluations. Aiming to overcome the shortcoming of stochastic optimization methods in the high calculation cost, a truss optimal method based on CMA-ES algorithm is proposed and applied to solve the section and shape optimization problems of trusses. The study results show that the method is feasible and has the advantages of high accuracy, high efficiency and easy implementation.

A Strength Pareto Gravitational Search Algorithm for Multi-Objective Optimization Problems

2015 ◽  
Vol 29 (06) ◽  
pp. 1559010 ◽  
Author(s):  
Xiaohui Yuan ◽  
Zhihuan Chen ◽  
Yanbin Yuan ◽  
Yuehua Huang ◽  
Xiaopan Zhang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Local Optima ◽  
Multi Objective ◽  
Fine Grained ◽  
Gravitational Search

A novel strength Pareto gravitational search algorithm (SPGSA) is proposed to solve multi-objective optimization problems. This SPGSA algorithm utilizes the strength Pareto concept to assign the fitness values for agents and uses a fine-grained elitism selection mechanism to keep the population diversity. Furthermore, the recombination operators are modeled in this approach to decrease the possibility of trapping in local optima. Experiments are conducted on a series of benchmark problems that are characterized by difficulties in local optimality, nonuniformity, and nonconvexity. The results show that the proposed SPGSA algorithm performs better in comparison with other related works. On the other hand, the effectiveness of two subtle means added to the GSA are verified, i.e. the fine-grained elitism selection and the use of SBX and PMO operators. Simulation results show that these measures not only improve the convergence ability of original GSA, but also preserve the population diversity adequately, which enables the SPGSA algorithm to have an excellent ability that keeps a desirable balance between the exploitation and exploration so as to accelerate the convergence speed to the true Pareto-optimal front.

Firefly Algorithm for Optimization Problem

2013 ◽  
Vol 421 ◽  
pp. 512-517 ◽  
Author(s):  
Nur Farahlina Johari ◽  
Azlan Mohd Zain ◽  
Mustaffa H. Noorfa ◽  
Amirmudin Udin
Keyword(s):  
Computer Science ◽  
Firefly Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Metaheuristic Algorithms ◽  
Science And Engineering ◽  
Multi Objective

This paper reviews the applications of Firefly Algorithm (FA) in various domain of optimization problem. Optimization is a process of determining the best solution to make something as functional and effective as possible by minimizing or maximizing the parameters involved in the problems. Several categories of optimization problem such as discrete, chaotic, multi-objective and many more are addressed by inspiring the behavior of fireflies as mentioned in the literatures. Literatures found that FA was mostly applied by researchers to solve the optimization problems in Computer Science and Engineering domain. Some of them are enhanced or hybridized with other techniques to discover better performance. In addition, literatures found that most of the cases that used FA technique have outperformed compare to other metaheuristic algorithms.

scholarly journals A New Modified Firefly Algorithm

2016 ◽  
Vol 4 (2) ◽  
pp. 18 ◽  
Author(s):  
Medha Gupta ◽  
Divya Gupta
Keyword(s):  
Firefly Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Collective Intelligence ◽  
Metaheuristic Algorithm ◽  
Numerical Studies ◽  
Modified Firefly Algorithm

<p class="Abstract"><span lang="EN-GB">Nature inspired meta-heuristic algorithms studies the emergent collective intelligence of groups of simple agents. </span><span lang="EN-AU">Firefly Algorithm is one of the new such swarm-based metaheuristic algorithm inspired by the flashing behavior of fireflies. The algorithm was first proposed in 2008 and since then has been successfully used for solving various optimization problems. In this work, we intend to propose a new modified version of Firefly algorithm (MoFA) and later its performance is compared with the standard firefly algorithm along with various other meta-heuristic algorithms. Numerical studies and results demonstrate that the proposed algorithm is superior to existing algorithms.</span></p>

scholarly journals Multi-objective particle swarm optimization with R2 indicator and adaptive method

2021 ◽  
Author(s):  
Qinghua Gu ◽  
Mengke Jiang ◽  
Song Jiang ◽  
Lu Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Empirical Studies ◽  
Experimental Studies ◽  
Particle Swarm ◽  
Adaptive Method ◽  
Swarm Optimization ◽  
Local Optima ◽  
Multi Objective ◽  
Problem Instances

AbstractMulti-objective particle swarm optimization algorithms encounter significant challenges when tackling many-objective optimization problems. This is mainly because of the imbalance between convergence and diversity that occurs when increasing the selection pressure. In this paper, a novel adaptive MOPSO (ANMPSO) algorithm based on R2 contribution and adaptive method is developed to improve the performance of MOPSO. First, a new global best solutions selection mechanism with R2 contribution is introduced to select leaders with better diversity and convergence. Second, to obtain a uniform distribution of particles, an adaptive method is used to guide the flight of particles. Third, a re-initialization strategy is proposed to prevent particles from trapping into local optima. Empirical studies on a large number (64 in total) of problem instances have demonstrated that ANMPSO performs well in terms of inverted generational distance and hyper-volume metrics. Experimental studies on the practical application have also revealed that ANMPSO could effectively solve problems in the real world.

A DYNAMIC POLYNOMIAL MUTATION FOR EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION ALGORITHMS

2011 ◽  
Vol 20 (01) ◽  
pp. 209-219 ◽  
Author(s):  
MOHAMMAD HAMDAN
Keyword(s):  
Performance Metrics ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Local Optima ◽  
Multi Objective ◽  
Multiobjective Optimization Problems ◽  
Evolutionary Optimization Algorithms ◽  
Dynamic Version ◽  
Polynomial Mutation ◽  
Multiobjective Algorithms

Polynomial mutation is widely used in evolutionary optimization algorithms as a variation operator. In previous work on the use of evolutionary algorithms for solving multi-objective problems, two versions of polynomial mutations were introduced. The first is non-highly disruptive that is not prone to local optima and the second is highly disruptive polynomial mutation. This paper looks at the two variants and proposes a dynamic version of polynomial mutation. The experimental results show that the proposed adaptive algorithm is doing well for three evolutionary multiobjective algorithms on well known multiobjective optimization problems in terms of convergence speed, generational distance and hypervolume performance metrics.

scholarly journals Improving firefly-based multi-objective optimization based on attraction law and crowding distance

2020 ◽  
Vol 8 (1) ◽  
pp. 229-241
Author(s):  
Farid Shayesteh ◽  
Reihaneh Kardehi Moghaddam
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Bees Algorithm ◽  
Test Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Crowding Distance ◽  
Simulation Results ◽  
Law Of Attraction

Multi-objective optimization problems are so designed that they simultaneously minimize several objectives functions (which are sometimes contradictory). In most cases, the objectives are in conflict with each other such that optimization of one objective does not lead to the optimization of another ones. Therefore, we should achieve a certain balance of goals to solve these problems, which usually requires the application of an intelligent method. In this regard, use of meta-heuristic algorithms will be associated with resolved problems. In this paper, we propose a new multi-objective firefly optimization method which is designed based on the law of attraction and crowding distance. The proposed methods efficiency has been evaluated by three valid test functions containing convex, nonconvex and multi discontinuous convex Pareto fronts. Simulation results confirm the significant accuracy of proposed method in defining the Pareto front for all three test functions. In addition, the simulation results indicates that proposed algorithm has higher accuracy and greater convergence speed, compared to other well known multi-objective algorithms such as non-dominated sorting genetic algorithm, Bees algorithm, Differential Evolution algorithm and Strong Pareto Evolutionary Algorithm.

scholarly journals An improved hybrid Aquila Optimizer and Harris Hawks Optimization for global optimization

2021 ◽  
Vol 18 (6) ◽  
pp. 7076-7109
Author(s):  
Shuang Wang ◽  
◽  
Heming Jia ◽  
Qingxin Liu ◽  
Rong Zheng ◽  
...  
Keyword(s):  
Global Optimization ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Space ◽  
Search Performance ◽  
Exploration And Exploitation ◽  
Design Problems ◽  
Local Optima ◽  
Opposition Based Learning ◽  
Engineering Design Problems

<abstract> <p>This paper introduces an improved hybrid Aquila Optimizer (AO) and Harris Hawks Optimization (HHO) algorithm, namely IHAOHHO, to enhance the searching performance for global optimization problems. In the IHAOHHO, valuable exploration and exploitation capabilities of AO and HHO are retained firstly, and then representative-based hunting (RH) and opposition-based learning (OBL) strategies are added in the exploration and exploitation phases to effectively improve the diversity of search space and local optima avoidance capability of the algorithm, respectively. To verify the optimization performance and the practicability, the proposed algorithm is comprehensively analyzed on standard and CEC2017 benchmark functions and three engineering design problems. The experimental results show that the proposed IHAOHHO has more superior global search performance and faster convergence speed compared to the basic AO and HHO and selected state-of-the-art meta-heuristic algorithms.</p> </abstract>

scholarly journals Verification of FPA and PSO algorithms for rule curve extraction and optimization of single- and multi-reservoir systems' operations considering their specific purposes

2020 ◽  
Author(s):  
Omid Bozorg-Haddad ◽  
Marzie Azad ◽  
Elahe Fallah-Mehdipour ◽  
Mohammad Delpasand ◽  
Xuefeng Chu
Keyword(s):  
Water Supply ◽  
Heuristic Algorithms ◽  
Optimal Operation ◽  
Optimal Solutions ◽  
Hydropower Generation ◽  
System Operation ◽  
Calculation Time ◽  
Reservoir System ◽  
Reservoir Systems ◽  
Rule Curve

Abstract The optimal operation of reservoirs is known as a complex issue in water resources management, which requires consideration of numerous variables (such as downstream water demand and power generation). For this optimization, researchers have used evolutionary and meta-heuristic algorithms, which are generally inspired by nature. These algorithms have been developed to achieve optimal/near-optimal solutions by a smaller number of function evaluations with less calculation time. In this research, the flower pollination algorithm (FPA) was used to optimize: (1) Aidoghmoush single-reservoir system operation for agricultural water supply, (2) Bazoft single-reservoir system operation for hydropower generation, (3) multi-reservoir system operation of Karun 5, Karun 4, and Bazoft, and (4) Bazoft single-reservoir system for rule curve extraction. To demonstrate the effectiveness of the FPA, it was first applied to solve the mathematical test functions, and then used to determine optimal operations of the reservoir systems with the purposes of downstream water supply and hydropower generation. In addition, the FPA was compared with the particle swarm optimization (PSO) algorithm and the non-linear programming (NLP) method. The results for the Aidoghmoush single-reservoir system showed that the best FPA solution was similar to the NLP solution, while the best PSO solution was about 0.2% different from the NLP solution. The best values of the objective function of the PSO were approximately 3.5 times, 28%, and 43% worse than those of the FPA for the Bazoft single-reservoir system for hydropower generation, the multi-reservoir system, and the Bazoft single-reservoir system for rule curve extraction, respectively. The FPA outperformed the PSO in finding the optimal solutions. Overall, FPA is one of the new evolutionary algorithms, which is capable of determining better (closer to the ideal solution) objective functions, decreasing the calculation time, simplifying the problem, and providing better solutions for decision makers.

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