Conventional Multi-objective Genetic Algorithms Research Articles

Suppression of stimulated Brillouin scattering effect in 2 μm narrow linewidth Tm3+-doped fiber amplifier based on phase-modulated waveform design

In this paper, we investigate the optimal design of phase modulation waveforms using the theoretical model of Tm3+-doped fiber amplifiers. The proposed design suppresses the SBS effect in the 2 μm-band Tm3+-doped fiber amplifiers with the help of a reference point-based multi-device non-dominated sequential genetic algorithm (NSGA-III). At the same time, synthesis efficiency is ensured. Different from the previous use of conventional multi-objective genetic algorithms to suppress the SBS effect in narrow linewidth amplifiers in the 1 μm band, the maximum SBS threshold is 121 W at a signal linewidth of 496 MHz with a more uniform distribution of Pareto solutions under this scheme. The signal linewidth of 680.61 MHz increases the SBS threshold by almost 20 W and the operating speed by several times compared to a conventional multi-objective genetic algorithm at different modulation depths, modulation frequencies and modulation periods. The threshold enhancement factor of the SBS signal with optimal modulation is 2.12 times that of the sinusoidal signal with an extended bandwidth of 700 MHz. The 2 μm laser obtained by this scheme enables more accurate and longer distance ranges in coherent detection.

Utilizing expected improvement and generalized data envelopment analysis in multi-objective genetic algorithms

Meta-heuristic methods such as genetic algorithms (GA) and particle swarm optimization (PSO) have been extended to multi-objective optimization problems, and have been observed to be useful for finding good approximate Pareto optimal solutions. In order to improve the convergence and the diversity in the search of solutions using meta-heuristic methods, this paper suggests a new method to make offspring by utilizing the expected improvement (EI) and generalized data envelopment analysis (GDEA). In addition, the effectiveness of the proposed method will be investigated through several numerical examples in comparison with the conventional multi-objective GA and PSO methods.

An Improved Kriging-Assisted Multi-Objective Genetic Algorithm

Although Genetic Algorithms (GAs) and Multi-Objective Genetic Algorithms (MOGAs) have been widely used in engineering design optimization, the important challenge still faced by researchers in using these methods is their high computational cost due to the population-based nature of these methods. For these problems it is important to devise MOGAs that can significantly reduce the number of simulation calls compared to a conventional MOGA. An improved kriging-assisted MOGA, called Circled Kriging MOGA (CK-MOGA), is presented in this paper, in which kriging metamodels are embedded within the computation procedure of a traditional MOGA. In the proposed approach, the decision as to whether the original simulation or its kriging metamodel should be used for evaluating an individual is based on a new and advanced objective switch criterion and an adaptive metamodeling technique. The effect of the possible estimated error from the metamodel is mitigated by applying the new switch criterion. Five numerical and engineering examples with different degrees of difficulty are used to illustrate applicability of the proposed approach, with the verification using different quality measures. The results show that, on the average, CK-MOGA outperforms both a conventional MOGA and a previously developed Kriging MOGA in terms of the number of simulation calls.

Improving multi-objective genetic algorithms with adaptive design of experiments and online metamodeling

Applications of multi-objective genetic algorithms (MOGAs) in engineering optimization problems often require numerous function calls. One way to reduce the number of function calls is to use an approximation in lieu of function calls. An approximation involves two steps: design of experiments (DOE) and metamodeling. This paper presents a new approach where both DOE and metamodeling are integrated with a MOGA. In particular, the DOE method reduces the number of generations in a MOGA, while the metamodeling reduces the number of function calls in each generation. In the present approach, the DOE locates a subset of design points that is estimated to better sample the design space, while the metamodeling assists in estimating the fitness of design points. Several numerical and engineering examples are used to demonstrate the applicability of this new approach. The results from these examples show that the proposed improved approach requires significantly fewer function calls and obtains similar solutions compared to a conventional MOGA and a recently developed metamodeling-assisted MOGA.

A Kriging Metamodel Assisted Multi-Objective Genetic Algorithm for Design Optimization

The high computational cost of population based optimization methods, such as multi-objective genetic algorithms (MOGAs), has been preventing applications of these methods to realistic engineering design problems. The main challenge is to devise methods that can significantly reduce the number of simulation (objective∕constraint functions) calls. We present a new multi-objective design optimization approach in which the Kriging-based metamodeling is embedded within a MOGA. The proposed approach is called Kriging assisted MOGA, or K-MOGA. The key difference between K-MOGA and a conventional MOGA is that in K-MOGA some of the design points are evaluated on-line using Kriging metamodeling instead of the actual simulation model. The decision as to whether the simulation or its Kriging metamodel should be used for evaluating a design point is based on a simple and objective criterion. It is determined whether by using the objective∕constraint functions’ Kriging metamodels for a design point, its “domination status” in the current generation can be changed. Seven numerical and engineering examples with different degrees of difficulty are used to illustrate applicability of the proposed K-MOGA. The results show that on the average K-MOGA converges to the Pareto frontier with an approximately 50% fewer number of simulation calls compared to a conventional MOGA.

Optimizing thermal design of data center cabinets with a new multi-objective genetic algorithm

There is an ever increasing need to use optimization methods for thermal design of data centers and the hardware populating them. Airflow simulations of cabinets and data centers are computationally intensive and this problem is exacerbated when the simulation model is integrated with a design optimization method. Generally speaking, thermal design of data center hardware can be posed as a constrained multi-objective optimization problem. A popular approach for solving this kind of problem is to use Multi-Objective Genetic Algorithms (MOGAs). However, the large number of simulation evaluations needed for MOGAs has been preventing their applications to realistic engineering design problems. In this paper, details of a substantially more efficient MOGA are formulated and demonstrated through a thermal analysis simulation model of a data center cabinet. First, a reduced-order model of the cabinet problem is constructed using the Proper Orthogonal Decomposition (POD). The POD model is then used to form the objective and constraint functions of an optimization model. Next, this optimization model is integrated with the new MOGA. The new MOGA uses a "kriging" guided operation in addition to conventional genetic algorithm operations to search the design space for global optimal design solutions. This approach for optimal design is essential to handle complex multi-objective situations, where the optimal solutions may be non-obvious from simple analyses or intuition. It is shown that in optimizing the data center cabinet problem, the new MOGA outperforms a conventional MOGA by estimating the Pareto front using 50% fewer simulation calls, which makes its use very promising for complex thermal design problems.

再使用宇宙輸送機フライバックブースタ翼空力形状の設計探査

The wing shape of flyback booster for a Two-Stage-To-Orbit reusable launch vehicle has been optimized considering four objectives. The objectives are to minimize the shift of aerodynamic center between supersonic and transonic conditions, transonic pitching moment and transonic drag coefficient, as well as to maximize subsonic lift coefficient. The three-dimensional Reynolds-averaged Navier-Stokes computation using the modified Spalart-Allmaras one-equation model is used in aerodynamic evaluation accounting for possible flow separations. Adaptive range multi-objective genetic algorithm is used for the present study because tradeoff can be obtained using a smaller number of individuals than conventional multi-objective genetic algorithms. Consequently, four-objective optimization has produced 102 non-dominated solutions, which represent tradeoff information among four objective functions. Moreover, Self-Organizing Maps have been used to analyze the present non-dominated solutions and to visualize tradeoffs and influence of design variables to the four objectives. Self-Organizing Maps contoured by the four objective functions and design variables are found to visualize tradeoffs and effects of each design variable.

Multi Agent Systems with Symbiotic Learning and Evolution -Masbiole- and Its Application.

Recently, systems are becoming more complex and larger than ever, so numerous attempts have been made to introduce biological features into artificial systems, because many biological systems in the nature exist as one of the most complex systems.Multi agent system with symbiotic learning and evolution have been recently proposed. It is named Masbiole. In this paper, Masbiole is reviewed and the method for evolving multi agent systems is proposed. From simulations on a multi objective knapsack problem, it has been clarified that Masbiole has better performance than that of conventional multi objective genetic algorithms.