Fast Non-dominated Sorting Genetic Algorithm Research Articles

Research on the Optimal Layout of High-strength Steel in the Transmission Tower

Abstract In order to research on the arrangement way of high-strength steel in ultrahigh voltage transmission towers, the integrated structure and material multi-objective optimization model of ultrahigh voltage transmission towers was established, and the optimization model is solved by using fast non-dominated sorting genetic algorithm (NSGA-II). Sectional areas and materials of each bar were regarded as the design variables, the structural min-cost was considered as the objective of the economic optimization, and the min-displacement of the control point was regarded as the objective of structural optimization. Based on the software MATLAB, relevant optimization program was programmed to solve the optimization model. The results show, the optimal results can satisfy the structural requirements and reduce the cost of projects, making the arrangement way of high-strength steel way in ultrahigh voltage transmission towers more reasonable and more economical.

Multi-objective harmony search algorithm for optimal power flow problem

This paper proposes a multi-objective harmony search (MOHS) algorithm for optimal power flow (OPF) problem. OPF problem is formulated as a non-linear constrained multi-objective optimization problem where different objectives and various constraints have been considered into the formulation. Fast elitist non-dominated sorting and crowding distance have been used to find and manage the Pareto optimal front. Finally, a fuzzy based mechanism has been used to select a compromise solution from the Pareto set. The proposed MOHS algorithm has been tested on IEEE 30 bus system with different objectives. Simulation results are also compared with fast non-dominated sorting genetic algorithm (NSGA-II) method. It is clear from the comparison that the proposed method is able to generate true and well distributed Pareto optimal solutions for OPF problem.

Hybrid Algorithm for JSP with Discretely Controllable Processing Times

Job-shop scheduling problem with discretely controllable processing times(JSP-DCPT) is a generalization of the classical Job-shop scheduling problem(JSP).To avoid solving the approximate problem of JSP-DCPT by using polynomial time approximation schemes,a hybrid algorithm is proposed to solve JSP-DCPT directly.Based on a decomposition approach,both the hybrid algorithm of tabu search and simulated annealing(TSSA) and the fast elitist non-dominated sorting genetic algorithm(NSGA-II) are nested within the proposed algorithm in order to effectively and efficiently solve the JSP and the discrete time-cost tradeoff problems decomposed from JSP-DCPT.Computational instance of different problem size is separately constructed on the basis of JSP benchmark FT06,FT10 and FT20.Experimental simulation results demonstrate that constrained Pareto frontiers can be obtained by using the proposed hybrid algorithm.

Solving multi-objective routing and wavelength assignment in WDM network using hybrid evolutionary computation approach

Routing and wavelength assignment (RWA) is a well-known issue in wavelength division multiplexing (WDM) optical networks. In this paper, we present RWA design for WDM networks by considering multiple design objectives which are maximizing the number of traffic demands to be served and minimizing the number of wavelength channels to be assigned. A hybrid evolutionary computation approach consisting of genetic algorithm for routing allocation with minimum degree first for wavelength assignment (GA-MDF) and the fast non-dominated sorting genetic algorithm (NSGA-II) to search for non-dominated solutions is applied for solving the multi-objective RWA network design problem. The hybrid evolutionary algorithm is used as a meta-heuristic technique for obtaining good solutions for various problem sizes. The obtained results are provided as candidate choices or non-dominated front. We compare the simulation results obtained from the NSGA-II with those obtained from the traditional Weighted-Sum approach. Numerical results show that our hybrid evolutionary computation approach is effective in solving the multi-objective RWA problem. The GA-MDF can outperform the FAR-FF method in both design objectives. In addition, the solutions from the NSGA-II are more diverse on the multi-objective space than those of the Weighted-Sum method. We also apply a Pruned mechanism to help cutting off numerous non-dominated solutions for making decision on the final solution.

MULTIOBJECTIVE DESIGN OF EQUIVALENT ACCELERATED LIFE TESTING PLANS

This paper is focused on the multiobjective design of equivalent accelerated life test (ALT) plans. Equivalent ALT plans are expected to achieve the same statistical performance as a baseline ALT plan yet lead to other desired performance measures such as reduced test time and total cost. Before determining the desired multiobjective equivalent ALT plans, an efficient fast non-dominated sorting genetic algorithm (NSGA-II) is utilized to identify a set of Pareto optimal solutions. To handle a large number of Pareto optimal solutions, a self-organizing map (SOM) and data envelopment analysis (DEA) are sequentially applied to classify the Pareto solutions and reduce the size of the suggested solution set. This integrated approach allows for the tradeoff of information among the Pareto solutions and the reduction in the size of the solution set. It provides a useful tool for practitioners to make meaningful decisions in planning ALT experiments.

Data Clustering of Solutions for Multiple Objective System Reliability Optimization Problems

This paper proposes a practical methodology for the solution of multi-objective system reliability optimization problems. The new method is based on the sequential combination of multi-objective evolutionary algorithms and data clustering on the prospective solutions to yield a smaller, more manageable sets of prospective solutions. Existing methods for multiple objective problems involve either the consolidation of all objectives into a single objective, or the determination of a Pareto-optimal set. In this paper, a new approach, involving post-Pareto clustering is proposed, offering a compromise between the two traditional approaches. In many real-life multi-objective optimization problems, the Pareto-optimal set can be extremely large or even contain an infinite number of solutions. Broad and detailed knowledge of the system is required during the decision making process in discriminating among the solutions contained in the Pareto-optimal set to eliminate the less satisfactory trade-offs and to select the most promising solution(s) for system implementation. The well-known reliability optimization problem, the redundancy allocation problem (RAP), was formulated as a multi-objective problem with the system reliability to be maximized, and cost and weight of the system to be minimized. A multiple stage process was performed to identify promising solutions. A Pareto-optimal set was initially obtained using the fast elitist nondominated sorting genetic algorithm (NSGA-II). The decision-making stage was then performed with the aid of data clustering techniques to prune the size of the Pareto-optimal set and obtain a smaller representation of the multi-objective design space; thereby making it easier for the decision-maker to find satisfactory and meaningful trade-offs, and to select a preferred final design solution.