17-03-2014, 06:45 PM
Abstract—In this paper, a novel Two-Level Evolutionary
Algorithm (TLEA) for solving function optimization
problems with inequality constraints is proposed. It
develops several new concepts and two new operator,
namely Big Mutation Operator and Reinitialization, and
introduce the Guo’s crossover operator as well, to
improve the convergence, and uses a two-level algorithm
framework, i.e., it uses the first level to fast locate the
domain that the global optimum exists, and uses the
second level to convergence to the global optimum. The
simulation results on some typical test problems show
that the algorithm proposed in this paper is better than
existing evolutionary algorithm in the accuracy of
solutions and efficiency of convergence.
Keywords-function optimization; evolutionary
algorithm
I. INTRODUCTION
Evolutionary algorithm [1] [2] [3] (EA) is firstly put
forward by Holland and his colleagues. It, based on
Darwin’s theory of the survival of the fittest and
Mendel’s theory of heredity, is one of the populationbased
approaches, and, with many good features such
as intelligence, concurrence, and robustness. It requires
little problem knowledge to set up an optimization
problem, and that individuals are created through semirandom
or random operators, such as mutation and/or
recombination, that are easy to implement. It is a most
effective method, which can be used to solve real
world problems with high complexity [4] [5] [6]. EA
starts from a so-called population, a group of
individuals, and use strategies like crossover, mutation
and selection to lead the population to a better
direction to get global optimum.
Constrained Optimization (CO) problems are
encountered in numerous applications. Structural
optimization, engineering design, VLSI design,
economics, allocation and location problems are just a
few of the scientific fields in which CO problems are
frequently met [7] [8] [9].
In this paper, a fast and efficient Two-Level
Evolutionary Algorithm (TLEA) is proposed to solve
function optimization problems with inequality
constraints. This new algorithm develops several new
concepts, and adopts two new operations, namely Big
Mutation Operator and Reinitialization, and introduces
Guo’s crossover operator to improve the convergence,
and uses a two-level algorithm framework. At the first
level, it uses some strategies to fast locate the domain
that the global optimum exists, and it uses the second
level to convergence to the global optimum at a high
accuracy. The simulation results on some typical test
problems show that the algorithm proposed in this
paper is better than existing evolutionary algorithm in
the efficiency.
The rest of the paper is organized as follows:
Section II formulates the CO problems. Section III
gives the details to construct the TLEA. Section IV is
the simulation experiment. In section V we make a
brief conclusion.
Algorithm (TLEA) for solving function optimization
problems with inequality constraints is proposed. It
develops several new concepts and two new operator,
namely Big Mutation Operator and Reinitialization, and
introduce the Guo’s crossover operator as well, to
improve the convergence, and uses a two-level algorithm
framework, i.e., it uses the first level to fast locate the
domain that the global optimum exists, and uses the
second level to convergence to the global optimum. The
simulation results on some typical test problems show
that the algorithm proposed in this paper is better than
existing evolutionary algorithm in the accuracy of
solutions and efficiency of convergence.
Keywords-function optimization; evolutionary
algorithm
I. INTRODUCTION
Evolutionary algorithm [1] [2] [3] (EA) is firstly put
forward by Holland and his colleagues. It, based on
Darwin’s theory of the survival of the fittest and
Mendel’s theory of heredity, is one of the populationbased
approaches, and, with many good features such
as intelligence, concurrence, and robustness. It requires
little problem knowledge to set up an optimization
problem, and that individuals are created through semirandom
or random operators, such as mutation and/or
recombination, that are easy to implement. It is a most
effective method, which can be used to solve real
world problems with high complexity [4] [5] [6]. EA
starts from a so-called population, a group of
individuals, and use strategies like crossover, mutation
and selection to lead the population to a better
direction to get global optimum.
Constrained Optimization (CO) problems are
encountered in numerous applications. Structural
optimization, engineering design, VLSI design,
economics, allocation and location problems are just a
few of the scientific fields in which CO problems are
frequently met [7] [8] [9].
In this paper, a fast and efficient Two-Level
Evolutionary Algorithm (TLEA) is proposed to solve
function optimization problems with inequality
constraints. This new algorithm develops several new
concepts, and adopts two new operations, namely Big
Mutation Operator and Reinitialization, and introduces
Guo’s crossover operator to improve the convergence,
and uses a two-level algorithm framework. At the first
level, it uses some strategies to fast locate the domain
that the global optimum exists, and it uses the second
level to convergence to the global optimum at a high
accuracy. The simulation results on some typical test
problems show that the algorithm proposed in this
paper is better than existing evolutionary algorithm in
the efficiency.
The rest of the paper is organized as follows:
Section II formulates the CO problems. Section III
gives the details to construct the TLEA. Section IV is
the simulation experiment. In section V we make a
brief conclusion.