Adaptive Random Search with Intensification and Diversification Combined with Genetic Algorithm

2006 ◽  
Vol 10 (6) ◽  
pp. 921-930 ◽  
Author(s):  
Dongkyu Sohn ◽  
◽  
Hiroyuki Hatakeyama ◽  
Shingo Mabu ◽  
Kotaro Hirasawa ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Random Search ◽  
Optimization Method ◽  
Objective Functions ◽  
Adaptive Random Search ◽  
Searching Ability

A novel optimization method named RasID-GA (an abbreviation of Adaptive Random Search with Intensification and Diversification combined with Genetic Algorithm) is proposed in order to enhance the searching ability of conventional RasID, which is a kind of Random Search with Intensification and Diversification. In this paper, the timing of switching from RasID to GA, or from GA to RasID is also studied. RasID-GA is compared with parallel RasIDs and GA using 23 different objective functions, and it turns out that RasID-GA performs well compared with other methods.

Optimization Method RasID-GA for Numerical Constrained Optimization Problems

2007 ◽  
Vol 11 (5) ◽  
pp. 469-477 ◽  
Author(s):  
Dongkyu Sohn ◽  
◽  
Shingo Mabu ◽  
Kotaro Hirasawa ◽  
Jinglu Hu
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Random Search ◽  
Optimal Solution ◽  
Solution Space ◽  
Optimization Method ◽  
Penalty Functions ◽  
Objective Functions ◽  
Constrained Optimization Problems

This paper proposes Adaptive Random search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA) for constrained optimization. In the previous work, we proposed RasID-GA which combines the best properties of RasID and Genetic Algorithm for unconstrained optimization problems. In general, it is very difficult to find an optimal solution for constrained optimization problems because their feasible solution space is very limited and they should consider the objective functions and constraint conditions. The conventional constrained optimization methods usually use penalty functions to solve given problems. But, it is generally recognized that the penalty function is hard to handle in terms of the balance between penalty functions and objective functions. In this paper, we propose a constrained optimization method using RasID-GA, which solves given problems without using penalty functions. The proposed method is tested and compared with Evolution Strategy with Stochastic Ranking using well-known 11 benchmark problems with constraints. From the Simulation results, RasID-GA can find an optimal solution or approximate solutions without using penalty functions.

Genetic Algorithm Based Optimal Planning Method of Energy Plant Configurations

1996 ◽  
Author(s):  
Kikuo Fujita ◽  
Shinsuke Akagi ◽  
Kiyotaka Yoshida ◽  
Noriyasu Hirokawa
Keyword(s):  
Genetic Algorithm ◽  
Energy Demand ◽  
Random Search ◽  
Optimization Method ◽  
Planning Problem ◽  
Optimal Planning ◽  
Search Performance ◽  
Energy Plant ◽  
Comparison Results ◽  
Plant Configuration

Abstract A genetic algorithm based optimization method is proposed for the planning problem of energy plant configurations. In such a planning problem, a plant configuration, i.e., types, models, and numbers of equipment, is determined so as to satisfy required energy demand conditions and to minimize the sum of plant facility cost and input energy cost. This is a combinatorial optimization problem similar to the knapsack problem, which is hard to find an optimal configuration for due to the huge number of alternatives. In this paper, we apply a genetic algorithm to such an optimal planning problem by representing a plant configuration with bit vectors and by arranging cost functions so as to keep search performance superior against the deceptive problem. The method is implemented on a symmetric shared-memory, multi-CPU workstation, where almost linear speedup is accomplished. Its performance is demonstrated by computational examples of a cogeneration plant and comparison results with a random search and a simulated annealing method.

scholarly journals Cooperative Task Assignment of a Heterogeneous Multi-UAV System Using an Adaptive Genetic Algorithm

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 687
Author(s):  
Fang Ye ◽  
Jie Chen ◽  
Yuan Tian ◽  
Tao Jiang
Keyword(s):  
Genetic Algorithm ◽  
Random Search ◽  
Task Assignment ◽  
Optimization Method ◽  
Adaptive Genetic Algorithm ◽  
Mutation Operators ◽  
Fitness Value ◽  
Coupling Constraints ◽  
Crossover And Mutation ◽  
And Task

The cooperative multiple task assignment problem (CMTAP) is an NP-hard combinatorial optimization problem. In this paper, CMTAP is to allocate multiple heterogeneous fixed-wing UAVs to perform a suppression of enemy air defense (SEAD) mission on multiple stationary ground targets. To solve this problem, we study the adaptive genetic algorithm (AGA) under the assumptions of the heterogeneity of UAVs and task coupling constraints. Firstly, the multi-type gene chromosome encoding scheme is designed to generate feasible chromosomes that satisfy the heterogeneity of UAVs and task coupling constraints. Then, AGA introduces the Dubins car model to simulate the UAV path formation and derives the fitness value of each chromosome. In order to comply with the chromosome coding strategy of multi-type genes, we designed the corresponding crossover and mutation operators to generate feasible offspring populations. Especially, the proposed mutation operators with the state-transition scheme enhance the stochastic searching ability of the proposed algorithm. Last but not least, the proposed AGA dynamically adjusts the number of crossover and mutation populations to avoid the subjective selection of simulation parameters. The numerical simulations verify that the proposed AGA has a better optimization ability and convergence effect compared with the random search method, genetic algorithm, ant colony optimization method, and particle search optimization method. Therefore, the effectiveness of the proposed algorithm is proven.

Parallel Dynamic Optimization of Steel Risers

2010 ◽  
Vol 133 (1) ◽  
Author(s):  
Rafael L. Tanaka ◽  
Clóvis de A. Martins
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Natural Selection ◽  
Dynamic Optimization ◽  
Solution Space ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Objective Functions ◽  
Definition Of

This paper addresses the use of optimization techniques in the design of a steel riser. Two methods are used: the genetic algorithm, which imitates the process of natural selection, and the simulated annealing, which is based on the process of annealing of a metal. Both of them are capable of searching a given solution space for the best feasible riser configuration according to predefined criteria. Optimization issues are discussed, such as problem codification, parameter selection, definition of objective function, and restrictions. A comparison between the results obtained for economic and structural objective functions is made for a case study. Optimization method parallelization is also addressed.

scholarly journals Development of a Genetic Algorithm Based Search Strategy Suited For Design Optimisation of Internal Combustion Engines

2008 ◽  
Vol 31 (1) ◽  
pp. 127-138
Author(s):  
N.G Nalitolela ◽  
H. Kadete
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Internal Combustion Engines ◽  
Design Space ◽  
Parametric Design ◽  
Random Search ◽  
Internal Combustion ◽  
Combustion Engines ◽  
Design Optimisation ◽  
Adaptive Random Search

Engine design optimisation is a multi-objective, multi-domain problem in a discontinuous design space. The state of the art of optimisation techniques shows that only methods of direct and adaptive search are appropriate for this type of problem. These include, adaptive random search, simulated annealing, evolution strategies and genetic algorithms. Ofthese methods, the genetic algorithms have been shown to be the most suited for the optimisation of multi-modal response functions in a discontinuous design space. This paper considers the important characteristics of genetic algorithms and their adaptation for use in parametric design optimisation of internal combustion engines. In order to verify the basicfunctionality of the proposed optimisation strategy, a genetic algorithm based, optimisation software was developed and tested on a number of analytical functions, selected from optimisation literature, with satisfactory results.

Fuzzy Neural Network Controller Based on Hybrid GA-BP Algorithm

2013 ◽  
Vol 823 ◽  
pp. 335-339 ◽  
Author(s):  
Yin Ping Chen ◽  
Hong Xia Wu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuzzy Neural Network ◽  
Random Search ◽  
Bp Algorithm ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Initial States ◽  
Searching Ability

This paper presents a hybrid GA-BP algorithm for fuzzy neural network controller (FNNC). BP algorithm is a method to monitor learning, easily realized and with good local searching ability. But it depends too much on the the initial states of the network. Genetic algorithm is a random search algorithm which has strong global searching ability. The hybrid GA-BP algorithm ensure the global convergence of learning by genetic algorithm, overcomes the BP algorithms dependency on the initial states on the one hand. On the other hand, combined with the BP algorithm overcome the simple genetic algorithms randomness, improve the searching efficiency. The simulations on the inverted pendulun problem show good performance and robustness of the proposed fuzzy neural network controller based on hybrid GA-BP algorithm.

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

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