Results 1 -
7 of
7
Choosing Leaders for Multi-objective PSO Algorithms Using Differential Evolution
"... Abstract. The fast convergence of particle swarm algorithms can become a downside in multi-objective optimization problems when there are many local optimal fronts. In such a situation a multi-objective particle swarm algorithm may get stuck to a local Pareto optimal front. In this paper we propose ..."
Abstract
-
Cited by 8 (3 self)
- Add to MetaCart
(Show Context)
Abstract. The fast convergence of particle swarm algorithms can become a downside in multi-objective optimization problems when there are many local optimal fronts. In such a situation a multi-objective particle swarm algorithm may get stuck to a local Pareto optimal front. In this paper we propose a new approach in selecting leaders for the particles to follow, which in-turn will guide the algorithm towards the Pareto optimal front. The proposed algorithm uses a Differential Evolution operator to create the leaders. These leaders can successfully guide the other particles towards the Pareto optimal front for various types of test problems. This simple yet robust algorithm is effective compared with existing multi-objective particle swarm algorithms.
A Multi-objective Approach to Testing Resource Allocation in Modular Software Systems
"... Abstract — Nowadays, as the software systems become increasingly large and complex, the problem of allocating the limited testing-resource during the testing phase has become more and more difficult. In this paper, we propose to solve the testing-resource allocation problem (TRAP) using multi-object ..."
Abstract
-
Cited by 4 (1 self)
- Add to MetaCart
(Show Context)
Abstract — Nowadays, as the software systems become increasingly large and complex, the problem of allocating the limited testing-resource during the testing phase has become more and more difficult. In this paper, we propose to solve the testing-resource allocation problem (TRAP) using multi-objective evolutionary algorithms. Specifically, we formulate TRAP as two multi-objective problems. First, we consider the reliability of the system and the testing cost as two objectives. In the second formulation, the total testing-resource consumed is also taken into account as the third goal. Two multi-objective evolutionary algorithms, Non-dominated Sorting Genetic Algorithm II (NSGA2) and Multi-Objective Differential Evolution Algorithms (MODE), are applied to solve the TRAP in the two scenarios. This is the first time that the TRAP is explicitly formulated and solved by multi-objective evolutionary approaches. Advantages of our approaches over the state-of-the-art single-objective approaches are demonstrated on two parallel-series modular software models.
A Multi-objective Evolutionary Approach to Aircraft Landing Scheduling Problems
"... Abstract—Scheduling aircraft landings has been a complex and challenging problem in air traffic control for long time. In this paper, we propose to solve the aircraft landing scheduling problem (ALSP) using multi-objective evolutionary algorithms (MOEAs). Specifically, we consider simultaneously min ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
(Show Context)
Abstract—Scheduling aircraft landings has been a complex and challenging problem in air traffic control for long time. In this paper, we propose to solve the aircraft landing scheduling problem (ALSP) using multi-objective evolutionary algorithms (MOEAs). Specifically, we consider simultaneously minimizing the total scheduled time of arrival and the total cost, and formulate the ALSP as a 2-objective optimization problem. A MOEA named Multi-Objective Neighborhood Search Differential Evolution (MONSDE) is applied to solve the 2-objective ALSP. Besides, a ranking scheme named non-dominated average ranking is also proposed to determine the optimal landing sequence. Advantages of our approaches are demonstrated on two example scenarios. I.
SEE PROFILE
, 2009
"... Abstract － In this paper, we propose a Multiobjective Self-adaptive Differential Evolution algorithm with objective-wise learning strategies (OW-MOSaDE) to solve numerical optimization problems with multiple conflicting objectives. The proposed approach learns suitable crossover parameter values and ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract － In this paper, we propose a Multiobjective Self-adaptive Differential Evolution algorithm with objective-wise learning strategies (OW-MOSaDE) to solve numerical optimization problems with multiple conflicting objectives. The proposed approach learns suitable crossover parameter values and mutation strategies for each objective separately in a multi-objective optimization problem. The performance of the proposed OW-MOSaDE algorithm is evaluated on a suit of 13 benchmark problems provided for the CEC2009 MOEA Special Session and Competition
355 PUBLICATIONS 13,818 CITATIONS SEE PROFILE
, 2007
"... Multi-objective optimization based on self-adaptive differential evolution algorithm ..."
Abstract
- Add to MetaCart
(Show Context)
Multi-objective optimization based on self-adaptive differential evolution algorithm
Self-adaptive Differential Evolution algorithm with
"... Abstract － In this paper, we propose a Multiobjective ..."
(Show Context)
Optimal Testing Resource Allocation Problems in Software System using Heuristic Algorithm
"... Abstract---Software Testing is the process of implementing a program with the definite intent of finding errors former to delivery to the end user. Due to the increase and the complexity of the software system the problem is how to optimally assign the narrow testing resource during the testing phas ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract---Software Testing is the process of implementing a program with the definite intent of finding errors former to delivery to the end user. Due to the increase and the complexity of the software system the problem is how to optimally assign the narrow testing resource during the testing phase has become more important and difficult. Traditional Optimal Testing Resource Allocation Problems (OTRAPs) includesin afinest allocation of a limited amount of testing resources with respect to reliability, cost etc. To solve the OTRAPs with Multi-Objective Algorithms called as Hierarchy Particle Swarm Optimization Algorithm (HPSO) is suggested. Especially, organize OTRAPs for two types of multi-objective problems. First one is reliability of the system and the testing cost of the system as two objectives. Second, the total testing resource consumed is also taken into account as the third objective, sensitivity is the fourth objective. The existing algorithms require more time and the both the evolutionary and the NSGA algorithm having more drawbacks. To overcome the drawbacks of the existing algorithm, the proposed HPSO algorithm which is used in this paper, satisfy allthe four objective of this research. Experimental results show that the proposed algorithm is more efficient than the existing algorithms. Keywords---Particle Swarm Optimization algorithm,
