Based on the analysis on the basic principles and characteristics of the existing multiobjective genetic algorithm (MOGA), an improved multi-objective GA with elites maintain is put forward based on non-dominated sorting genetic algorithm (NSGA). NSGA-II algorithm theory and parallel hybrid evolutionary theory is described in detail. The design principle, process and detailed implementations of...

This paper presents the integration between two types of genetic algorithm: a multi-objective genetic algorithm (MOGA) and a co-operative co-evolutionary genetic algorithm (CCGA). The resulting algorithm is referred to as a multi-objective co-operative co-evolutionary genetic algorithm or MOCCGA. The integration between the two algorithms is carried out in order to improve the performance of th...

This paper describes the implementation of Single-objective Genetic Algorithm (SOGA) and Multi-objectives Genetic Algorithm (MOGA) to optimize the pipelined Fast Fourier Transform (FFT) coefficient in order to improve the performance of Signal to Noise Ratio (SNR) and also the Switching Activity (SA). The SA and SNR are optimized separately in a Radix-4 Single Path Delay Feedback (R4SDF) pipeli...

-This paper presents a multiobjective genetic algorithm (MOGA)to solve the train crew pairing problem in railway companies. The proposed MOGA has several features, such as 1) A permutation-based model is proposed rather than the 0-1 set partition model. 2) Instead of pre-assigning a fixed group number of crewmembers, the proposed method can determine it by performing the evolutionary process. 3...

The advances in biological technologies make it possible to generate data for multiple conditions simultaneously. Discovering the condition-specific modules in multiple networks has great merit in understanding the underlying molecular mechanisms of cells. The available algorithms transform the multiple networks into a single objective optimization problem, which is criticized for its low accur...

PurposeThe purpose of this paper is to present a hierarchical circuit synthesis system with a Hybrid DLO-MOGA (Deterministic Local Optimization Multi-Objective Genetic Algorithm) optimization scheme for system level synthesis. Design/methodology/approachThe use of a local optimization with a deterministic algorithm based on linear equations which is computationally efficient and improves the fe...

This paper presents the nonlinear time series prediction using Lyapunov theory-based fuzzy neural network and multi-objective genetic algorithm (MOGA). The architecture employs fuzzy neural network (FNN) structure and the tuning of the parameters of FNN using the combination of the MOGA and the modified Lyapunov theory-based adaptive filtering algorithm (LAF). The proposed scheme has been used ...

Recognising the multiobjective nature of the decision process for rehabilitation of water supply distribution systems, this paper presents a comparative study of two multiobjective evolutionary methods, namely, multiobjective genetic algorithm (MOGA) and strength Pareto evolutionary algorithm (SPEA). The analyses were conducted on a simple hypothetical network for cost minimisation and minimum ...

Multi-objective genetic algorithms (MOGA) are used to optimize a low-thrust spacecraft control law for orbit transfers around a central body. A Lyapunov feedback control law called the Q-law is used to create a feasible orbit transfer. Then, the parameters in the Q-law are optimized with MOGAs. The optimization goal is to minimize both the flight time and the consumed propellant mass of the tra...

Multi-label spatial classification based on association rules with multi objective genetic algorithms (MOGA) enriched by semi supervised learning is proposed in this paper. It is to deal with multiple class labels problem. In this paper we adapt problem transformation for the multi label classification. We use hybrid evolutionary algorithm for the optimization in the generation of spatial assoc...