Recombination and Self-Adaptation in Multi-objective Genetic Algorithms

This paper investigates the influence of recombination and self-adaptation in real-encoded Multi-Objective Genetic Algorithms (MOGAs). NSGA-II and SPEA2 are used as example to characterize the efficiency of MOGAs in relation to various recombination operators. The blend crossover, the simulated binary crossover and the breeder genetic crossover are compared for both MOGAs on multi-objective problems of the literature. Finally, a self-adaptive recombination scheme is proposed to improve the robustness of MOGAs

Damage estimation using multi objective genetic algorithms

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Path Relinking in Pareto Multi-objective Genetic Algorithms

Path relinking algorithms have proved their efficiency in single objective optimization. Here we propose to adapt this concept to Pareto optimization. We combine this original approach to a genetic algorithm. By applying this hybrid approach to a bi-objective permutation flow-shop problem, we show the interest of this approach. In this paper, we present first an Adaptive Genetic Algorithm dedicated to obtain a first well diversified approximation of the Pareto set. Then, we present an original hybridization with Path Relinking algorithm, in order to intensify the search between solutions obtained by the first approach. Results obtained are promising and show that cooperation between these optimization methods could be efficient for Pareto optimization

A Hardware Implementation Method of Multi-Objective Genetic Algorithms

CEC2006 : IEEE International Conference on Evolutionary Computation , Jul 16-21, 2006 , Vancouver, BC, CanadaMulti-objective genetic algorithms (MOGAs) are approximation techniques to solve multi-objective optimization problems. Since MOGAs search a wide variety of pareto optimal solutions at the same time, MOGAs require large computation power. In order to solve practical sizes of the multi objective optimization problems, it is desirable to design and develop a hardware implementation method for MOGAs with high search efficiency and calculation speed. In this paper, we propose a new method to easily implement MOGAs as high performance hardware circuits. In the proposed method, we adopt simple Minimal Generation Gap (MGG) model as the generation model, because it is easy to be pipelined. In order to preserve diversity of individuals, we need a special selection mechanism such as the niching method which takes large computation time to repeatedly compare superiority among all individuals in the population. In the proposed method, we developed a new selection mechanism which greatly reduces the number of comparisons among individuals, keeping diversity of individuals. Our method also includes a parallel execution architecture based on Island GA which is scalable to the number of concurrent pipelines and effective to keep diversity of individuals. We applied our method to multi-objective Knapsack Problem. As a result, we confirmed that our method has higher search efficiency than existing method

Optimization of piezoelectric patches in smart structures using multi-objective genetic algorithms

In this paper multi-objective genetic algorithms have been used to search for the optimal placement of the piezoelectric sensors and actuators bonded on smart beams. A finite element method based on Timoshenko beam theory is used accounting for the piezoelectric layers. The discrete optimal sensor and actuator location problem is formulated in the framework of a zero-one optimization problem with multi-objective functions as performance measures. A cantilever beam example is considered to demonstrate the performance of the selected multi-objective genetic algorithm which is NSGAII. It is shown that the proposed algorithm is effective in developing optimal Pareto front curves for optimal placement and number of actuators and sensors such that the performance on dynamic responses is also satisfied

Simultaneous Assembly Planning and Assembly System Design Using Multi-objective Genetic Algorithms

This paper aims to demonstrate the application of multi-objective evolutionary optimization, namely an adaptation of NSGA-II, to simultaneously optimize the assembly sequence plan as well as selection of the type and number of assembly stations for a production shop that produces three different models of wind propelled ventilators. The decision variables, which are the assembly sequences of each product and the machine selection at each assembly station, are encoded in a manner that allows efficient implementation of a repair operator to maintain the feasibility of the offspring. Test runs are conducted for the sample assembly system using a crossover operator tailored for the proposed encoding and some conventional crossover schemes. The results show overall good performance for all schemes with the best performance achieved by the tailored crossover, which illustrates the applicability of multi-objective GAÕs. The presented framework proposed is generic to be applicable to other products and assembly systems.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87283/4/Saitou97.pd

Multi-objective genetic algorithms for scheduling of radiotherapy treatments for categorised cancer patients

Abstract. This paper presents a multi-objective optimisation model and algorithms for scheduling of radiotherapy treatments for categorised cancer patients. The model is developed considering real life radiotherapy treatment processes at Arden Cancer Centre, in the UK. The scheduling model considers various real life constraints, such as doctors ’ rota, machine availability, patient’s category, waiting time targets, (i.e., the time when a patient should receive the first treatment fraction), and so on. Two objectives are defined: minimisation of the Average patient’s waiting time and minimisation of Average length of breaches of waiting time targets. Three Genetic Algorithms (GAs) are developed and implemented which treat radiotherapy patient categories, namely emergency, palliative and radical patients in different ways: (1) Standard-GA, which considers all patient categories equally, (2) KB-GA, which has an embedded knowledge on the scheduling of emergency patient category and (3) Weighted-GA, which operates with different weights given to the patient categories. The performance of schedules generated by using the three GAs is compared using the statistical analyses. The results show that KB-GA generated the schedules with best performance considering emergency patients and slightly outperforms the other two GAs when all patient categories are considered simultaneously. KB-GA and Standard-GA generated better performance schedules for emergency and palliative patient

Real-time energy optimization of irrigation scheduling by parallel multi-objective genetic algorithms

[EN] The present work is motivated by the need to reduce the energy costs arising from the pressure demands of drip and sprinkling irrigation, compounded by the increase in the energy price in recent years. Researchers have demonstrated that proper operation of the irrigation network reduces associated pumping costs. The main challenge was to obtain the optimal operation parameters on near real-time due to the fact that the high complexity of the optimization problem requires a great computational effort. The classic approach to the problem imposes a strict fulfilment of minimum pressures as a restriction. This study, however, presents a new methodology for the reordering of irrigation scheduling, incorporating the constraint of daily volume requests for each hydrant. The methodology is capable of minimizing the cost of energy while maximizing pressures at the critical hydrants. Cost reductions of about 6¿7% were reached for scenarios without pressure deficit for the case study. Greater computational efficiency was achieved by posing the problem from a multi-objective approach, on the one hand, and by establishing the parallel evaluation of the objective function, on the other. The speed-up obtained by combining a reduction in the number of function evaluations thanks to the faster convergence of the multi-objective approach and the reduction of the computational time due to the parallelization of the algorithm achieved results about 10 times faster. This improvement allowed the tool to be implemented for the daily optimization of irrigation requests.This work has been supported by the VALi+D R&D Program of the Generalitat Valenciana (Spain).Alonso-Campos, J.; Jiménez Bello, MA.; Martínez Alzamora, F. (2020). Real-time energy optimization of irrigation scheduling by parallel multi-objective genetic algorithms. Agricultural Water Management. 227:1-8. https://doi.org/10.1016/j.agwat.2019.105857S1822

Multi-objective genetic algorithms for scheduling mateiral handling equipment at automated air cargo terminals

In order to improve thc productivitics of a typical cargo handling system, it is important to reduce the waiting time of stacker crancs (SCs) and the total traveling time of automated guided vehicles (AGVs) through efficient scheduling of SCs and ACVs, which are cooperating tightly to perform cargo handling operations in an optimal way. In this paper, we devclop and investigate the application of the multi-objective genetic algorithm (MOCA) to solve such schcduling problem with the objectives of minimizing the ACV total traveling time and thc total delay time of the SC. The results of the experimcnts demonstrated that MOGA produces better solution than the single objective genetic algorithms.published_or_final_versio