Multi-objective Evolutionary Search Research Articles

Optimal design for passive suspension of a light rail vehicle using constrained multiobjective evolutionary search

This research proposes a systematic and effective optimization process for the design of vertical passive suspension of light rail vehicles (LRVs) using new constrained multiobjective evolution algorithms. A multibody dynamic model of the three-car train set is presented and the suspension spring and damping parameters are optimally designed. A new design of the passive suspension is aided by the use of evolution algorithms to attain the best compromise between ride quality and suspension deflections due to irregular gradient tracks. Extensive simulations are performed to verify the proposed design scheme. The preliminary results show that, when the passive suspension is optimized via the proposed approach, a substantial improvement in the vertical ride quality is obtained while keeping the suspension deflections within their allowable clearance when the light rail vehicle runs onto the worst track condition.

A TSP-GA multi-objective algorithm for flow-shop scheduling

A multi-objective evolutionary search algorithm using a travelling salesman algorithm and genetic algorithm for flow-shop scheduling is proposed in this paper. The initial sequence is obtained by solving the TSP. The initial population of the genetic algorithm is created with the help of a neighbourhood creation scheme known as a random insertion perturbation scheme, which uses the sequence obtained from TSP. The proposed algorithm uses a weighted sum of multiple objectives as a fitness function. The weights are randomly generated for each generation to enable a multi-directional search. The performance measures considered include minimising makespan, mean flow time and machine idle time. The performance of the proposed algorithm is demonstrated by applying it to benchmark problems available in the OR-Library.

A hybrid heuristic approach to discrete multi-objective optimization of credit portfolios

A hybrid heuristic approach combining multi-objective evolutionary and problem-specific local search methods is proposed to support the risk-return analysis of credit portfolios. Its goal is to compute approximations of discrete sets of Pareto-efficient portfolio structures concerning both the respective portfolio return and the respective portfolio risk using the non-linear, non-convex Credit-Value-at-Risk downside risk measure which is relevant to real world credit portfolio optimization. In addition, constraints like capital budget restrictions are considered in the hybrid heuristic framework. The computational complexity of selected parts of the algorithm is analyzed. Moreover, empirical results indicate that the hybrid method is superior in convergence speed to a non-hybrid evolutionary approach and finds approximations of risk-return efficient portfolios within reasonable time.

A GA-SA Multiobjective Hybrid Search Algorithm for Integrating Lot Sizing and Sequencing in Flow-Line Scheduling

It is very important that the sequencing and lot sizing in the flow-line environment are not independent. They should be integrated. In this paper, a multiobjective hybrid evolutionary search algorithm which combines a genetic algorithm and a simulated annealing algorithm is proposed and the performance of the proposed algorithm is compared with the existing genetic algorithm and the simulated annealing algorithm. The algorithms are coded independently and the performance is compared with randomly generated test problems. The objective functions considered for evaluation are the minimisation of makespan, minimisation of overtime and minimisation of holding cost. The scalar fitness function combining all the three objective functions, which minimises total cost, is used. The results are presented in tables and figures. The results show that the proposed hybrid algorithm performs better than the genetic algorithm and the simulated annealing algorithm.