Large-scale Multi-objective Optimization Research Articles

A Novel Intelligent Multiobjective Simulated Annealing Algorithm for Designing Robust PID Controllers

This paper proposes an intelligent multiobjective simulated annealing algorithm (IMOSA) and its application to an optimal proportional integral derivative (PID) controller design problem. A well-designed PID-type controller should satisfy the following objectives: 1) disturbance attenuation; 2) robust stability; and 3) accurate setpoint tracking. The optimal PID controller design problem is a large-scale multiobjective optimization problem characterized by the following: 1) nonlinear multimodal search space; 2) large-scale search space; 3) three tight constraints; 4) multiple objectives; and 5) expensive objective function evaluations. In contrast to existing multiobjective algorithms of simulated annealing, the high performance in IMOSA arises mainly from a novel multiobjective generation mechanism using a Pareto-based scoring function without using heuristics. The multiobjective generation mechanism operates on a high-score nondominated solution using a systematic reasoning method based on an orthogonal experimental design, which exploits its neighborhood to economically generate a set of well-distributed nondominated solutions by considering individual and overall objectives. IMOSA is evaluated by using a practical design example of a super-maneuverable fighter aircraft system. An efficient existing multiobjective algorithm, the improved strength Pareto evolutionary algorithm, is also applied to the same example for comparison. Simulation results demonstrate high performance of the IMOSA-based method in designing robust PID controllers.

Optimal Reliability-Design of a Series-Parallel System by a Large-Scale Multiobjective Optimization Method

This paper proposes a large-scale multiobjective optimization method for a series-parallel system by applying both the surrogate worth trade-off method and the dual decomposition method in order to obtain the preferred solution of the decision maker. A numerical example for a system composed of 16 subsystems operating in series is solved efficiently by the proposed method. The details of the example are in a separately available Supplement.

Reliability design of a standby system by a large-scale multiobjective optimization method: M. Sakawa. Microelectron. Reliab.20, 191 (1980)

Reliability design of a standby system by a large-scale multiobjective optimization method: M. Sakawa. Microelectron. Reliab.20, 191 (1980)

Reliability design of a standby system by a large-scale multiobjective optimization method

This paper deals with the reliability design of a standby redundancy system from a view point of large-scale multiobjective optimization. A large-scale multiobjective mathematical model is formulated and the problem considered is to optimize system reliability, cost, weight and volume, for a given mission time, subject to multiple constraints. A large-scale multiobjective optimization method is proposed by combined application of both surrogate worth trade-off method and dual decomposition method, and the preferred solution of the decision maker can effectively be obtained by using the proposed method. The method is illustrated by numerical examples.

Decomposition Approaches to Large-Scale Multiobjective Reliability Design

This paper considers the large-scale multi objective reliability design of a series system from a view point of dual decomposition. A large-scale multiobjective mathematical model is formulated and the problem considered is to assign both reliability and redundancy to each subsystem such that the system reliability, cost, weight, volume and product of weight and volume are optimized subject to multiple constraints. A large-scale multiobjective optimization method is proposed by combined application of both the surrogate worth trade-off method and the dua decomposition method, and the preferred solution of the decision maker can effectively be obtained by applying the proposed method. The efficiency of the proposed method is illustrated by a numerical example.

Interactive Multiobjective Decisionmaking for Large-Scale Systems and Its Application to Environmental Systems

A new interactive multiobjective decisionmaking technique for solving large-scale multiobjective optimization problems is presented. Our method combines the desirable features of both the surrogate worth trade-off (SWT) method and the multiattribute utility function (MUF) method by incorporating the dual decomposition method to obtain Paretooptimal solutions. By assessing the marginal rates of substitution of the decisionmaker (DM) and maximizing the local proxy preference functions sequentially, we can interactively derive his preferred solution efficiently from among the Pareto-optimal solutions obtained by the dual decomposition method. The industrial pollution control problem in industrialized Osaka City in Japan is formulated, and it is demonstrated that the problem can be effectively solved by the proposed method.