scholarly journals A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Ilyas Benmessahel ◽  
Kun Xie ◽  
Mouna Chellal
Keyword(s):  
Optimization Technique ◽  
Multiobjective Problems ◽  
Single Objective

Enhancement of Labyrinth Seals Efficiency by Means of a Multi-Objective Optimization Technique

2018 ◽  
Author(s):  
Mikhail Gritckevich ◽  
Kunyuan Zhou ◽  
Vincent Peltier ◽  
Markus Raben ◽  
Olga Galchenko
Keyword(s):  
Optimization Technique ◽  
Leakage Flow ◽  
Multi Objective Optimization ◽  
Labyrinth Seals ◽  
Engine Operation ◽  
Turbine Engine ◽  
Multi Objective ◽  
Single Objective ◽  
Ansys Workbench ◽  
Comprehensive Study

A comprehensive study of several labyrinth seals has been performed in the framework of both single-objective and multi-objective optimizations with the main focus on the effect of stator grooves formed due to the rubbing during gas turbine engine operation. For that purpose, the developed optimization workflow based on the DLR-AutoOpti optimizer and ANSYS-Workbench CAE environment has been employed to reduce the leakage flow and windage heating for several seals. The obtained results indicate that the seal designs obtained from optimizations without stator grooves have worse performance during the lifecycle than those with the stator grooves, justifying the importance of considering this effect for real engineering applications.

scholarly journals Optimal Model for Complementary Operation of a Photovoltaic-Wind-Pumped Storage System

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Jinjin Gao ◽  
Yuan Zheng ◽  
Jianming Li ◽  
Xiaoming Zhu ◽  
Kan Kan
Keyword(s):  
Wind Power ◽  
Storage System ◽  
Optimal Solution ◽  
Multiobjective Problems ◽  
Storage Station ◽  
Compromise Optimal Solution ◽  
Pumped Storage ◽  
Economic Objective ◽  
Single Objective

An optimization model for the complementary operation of a photovoltaic-wind-pumped storage system is built to make full use of solar and wind energy. Apart from ensuring the maximum economic benefit which is normally used as the only objective, the stable objectives of minimizing the output fluctuation and variation of load and output difference are added to form the multiobjective problems because of lack of study on access capacity of photovoltaic and wind power. The model aims to increase the power benefit and reduce the output fluctuation and variation of load and output difference under the constraints of station, output balance, and transmission limitation. In a case study, four schemes including single-objective independent operation, single-objective complementary operation, and multiobjective complementary operation are compared to discuss the effect of pumped storage station on economic objective and stable objectives. Furthermore, the opposite trend of the two objectives is proved and a compromise optimal solution is given. The results indicate that the pumped storage station can effectively increase power benefit and access capacity of photovoltaic and wind power. The study can provide references to the complementary optimization of the pumped storage station and the intermittent renewable energy.

scholarly journals A NEW PROBABILISTIC ALGORITHM FOR SOLVING A CLASS OF SINGLE OR MULTI-OBJECTIVE OPTIMAL PROBLEMS

2009 ◽  
Vol 12 (11) ◽  
pp. 11-26
Author(s):  
Hao Van Tran ◽  
Thong Huu Nguyen
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Fixed Number ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Probabilistic Algorithm ◽  
Multi Objective ◽  
Single Objective

We consider a class of single-objective optimization problems which haves the character: there is a fixed number k (1≤k<n) that is independent of the size n of the problem such that if we only need to change values of k variables then it has the ability to find a better solution than the current one, let us call it Ok. In this paper, we propose a new numerical optimization technique, Search Via Probability (SVP) algorithm, for solving single objective optimization problems of the class Ok. The SVP algorithm uses probabilities to control the process of searching for optimal solutions. We calculate probabilities of the appearance of a better solution than the current one on each of iterations, and on the performance of SVP algorithm we create good conditions for its appearance. We tested this approach by implementing the SVP algorithm on some test single-objective and multi objective optimization problems, and we found good and very stable results.

scholarly journals Search Dynamics on Multimodal Multiobjective Problems

2019 ◽  
Vol 27 (4) ◽  
pp. 577-609 ◽  
Author(s):  
P. Kerschke ◽  
H. Wang ◽  
M. Preuss ◽  
C. Grimme ◽  
A. H. Deutz ◽  
...  
Keyword(s):  
Multiobjective Optimization ◽  
Local Search ◽  
Objective Functions ◽  
Test Bed ◽  
Decision Space ◽  
Multiobjective Problems ◽  
Definition Of ◽  
Visualization Techniques ◽  
Single Objective ◽  
Selection Of

We continue recent work on the definition of multimodality in multiobjective optimization (MO) and the introduction of a test bed for multimodal MO problems. This goes beyond well-known diversity maintenance approaches but instead focuses on the landscape topology induced by the objective functions. More general multimodal MO problems are considered by allowing ellipsoid contours for single-objective subproblems. An experimental analysis compares two MO algorithms, one that explicitly relies on hypervolume gradient approximation, and one that is based on local search, both on a selection of generated example problems. We do not focus on performance but on the interaction induced by the problems and algorithms, which can be described by means of specific characteristics explicitly designed for the multimodal MO setting. Furthermore, we widen the scope of our analysis by additionally applying visualization techniques in the decision space. This strengthens and extends the foundations for Exploratory Landscape Analysis (ELA) in MO.

scholarly journals A Novel Economic Dispatch in Power Grids Based on Enhanced Firework Algorithm

2019 ◽  
Vol 3 (4) ◽  
Author(s):  
Iman Niazazari ◽  
Oveis Asgari Asgari Gashteroodkhani ◽  
Amir Niaz Azari
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Test System ◽  
Power Grids ◽  
Reliability Test ◽  
Generation Cost ◽  
Swarm Intelligence Algorithm ◽  
Single Objective

This paper proposes a novel single objective optimization technique for economic dispatch (ED) in power grids. This new technique is developed based on firework algorithm (FWA) and is implemented in the IEEE 24 bus reliability test system. In this paper, the single-objective enhanced fireworks (EFWA) is developed to find the economic operating condition to minimize the generation cost. This method is a swarm intelligence algorithm that solves a single-objective optimization problem much faster than other well-known algorithms such as genetic algorithm (GA). The experimental results show that the proposed EFWA method is indeed capable of obtaining higher quality solutions efficiently in ED problems.

Population Diversity of Particle Swarm Optimizer Solving Single- and Multi-Objective Problems

2015 ◽  
pp. 71-98 ◽  
Author(s):  
Shi Cheng ◽  
Yuhui Shi ◽  
Quande Qin
Keyword(s):  
Swarm Intelligence ◽  
Particle Swarm ◽  
Population Diversity ◽  
Exploration And Exploitation ◽  
Adaptive Optimization ◽  
Particle Swarm Optimizer ◽  
Multiobjective Problems ◽  
Computational Resources ◽  
Swarm Intelligence Algorithm ◽  
Single Objective

Premature convergence occurs in swarm intelligence algorithms searching for optima. A swarm intelligence algorithm has two kinds of abilities: exploration of new possibilities and exploitation of old certainties. The exploration ability means that an algorithm can explore more search places to increase the possibility that the algorithm can find good enough solutions. In contrast, the exploitation ability means that an algorithm focuses on the refinement of found promising areas. An algorithm should have a balance between exploration and exploitation, that is, the allocation of computational resources should be optimized to ensure that an algorithm can find good enough solutions effectively. The diversity measures the distribution of individuals' information. From the observation of the distribution and diversity change, the degree of exploration and exploitation can be obtained. Another issue in multiobjective is the solution metric. Pareto domination is utilized to compare two solutions; however, solutions are almost Pareto non-dominated for multiobjective problems with more than ten objectives. In this chapter, the authors analyze the population diversity of a particle swarm optimizer for solving both single objective and multiobjective problems. The population diversity of solutions is used to measure the goodness of a set of solutions. This metric may guide the search in problems with numerous objectives. Adaptive optimization algorithms can be designed through controlling the balance between exploration and exploitation.

scholarly journals A Modified Dragonfly Optimization Algorithm for Single- and Multiobjective Problems Using Brownian Motion

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Çiğdem İnan Acı ◽  
Hakan Gülcan
Keyword(s):  
Brownian Motion ◽  
Optimization Techniques ◽  
Dragonfly Algorithm ◽  
Original Algorithm ◽  
Random Flights ◽  
Search Area ◽  
Multiobjective Problems ◽  
Beam Design ◽  
Optimum Cost ◽  
Single Objective

The dragonfly algorithm (DA) is one of the optimization techniques developed in recent years. The random flying behavior of dragonflies in nature is modeled in the DA using the Levy flight mechanism (LFM). However, LFM has disadvantages such as the overflowing of the search area and interruption of random flights due to its big searching steps. In this study, an algorithm, known as the Brownian motion, is used to improve the randomization stage of the DA. The modified DA was applied to 15 single-objective and 6 multiobjective problems and then compared with the original algorithm. The modified DA provided up to 90% improvement compared to the original algorithm’s minimum point access. The modified algorithm was also applied to welded beam design, a well-known benchmark problem, and thus was able to calculate the optimum cost 20% lower.

scholarly journals Improvement of Interior Ballistic Performance Utilizing Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hazem El Sadek ◽  
Xiaobing Zhang ◽  
Mahmoud Rashad ◽  
Cheng Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Feasible Region ◽  
Evolution Operators ◽  
Ballistic Performance ◽  
Swarm Optimization ◽  
Charge Design ◽  
Single Objective

This paper investigates the interior ballistic propelling charge design using the optimization methods to select the optimum charge design and to improve the interior ballistic performance. The propelling charge consists of a mixture propellant of seven-perforated granular propellant and one-hole tubular propellant. The genetic algorithms and some other evolutionary algorithms have complex evolution operators such as crossover, mutation, encoding, and decoding. These evolution operators have a bad performance represented in convergence speed and accuracy of the solution. Hence, the particle swarm optimization technique is developed. It is carried out in conjunction with interior ballistic lumped-parameter model with the mixture propellant. This technique is applied to both single-objective and multiobjective problems. In the single-objective problem, the optimization results are compared with genetic algorithm and the experimental results. The particle swarm optimization introduces a better performance of solution quality and convergence speed. In the multiobjective problem, the feasible region provides a set of available choices to the charge’s designer. Hence, a linear analysis method is adopted to give an appropriate set of the weight coefficients for the objective functions. The results of particle swarm optimization improved the interior ballistic performance and provided a modern direction for interior ballistic propelling charge design of guided projectile.

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