Improving convergence of evolutionary multi-objective optimization with local search: a concurrent-hybrid algorithm

Sindhya, Karthik; Deb, Kalyanmoy; Miettinen, Kaisa

doi:10.1007/s11047-011-9250-4

Cited by 34 publications

(18 citation statements)

References 35 publications

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“…Among scalarizing functions, weighted sum of objectives is the most common one, which is first employed in the well-known multiobjective genetic local search (MOGLS) [41]. Recently, Sindhya et al [42] presented a comprehensive literature review on MAs for the multiobjective optimization with different scalarizing functions adopted.…”

Section: Memetic Algorithms For Multiobjective Combinatorial Optimmentioning

confidence: 99%

Multiobjective Flexible Job Shop Scheduling Using Memetic Algorithms

Yuan

2015

IEEE Trans. Automat. Sci. Eng.

222

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In this paper, we propose new memetic algorithms (MAs) for the multiobjective flexible job shop scheduling problem (MO-FJSP) with the objectives to minimize the makespan, total workload, and critical workload. The problem is addressed in a Pareto manner, which aims to search for a set of Pareto optimal solutions. First, by using well-designed chromosome encoding/decoding scheme and genetic operators, the nondominated sorting genetic algorithm II (NSGA-II) is adapted for the MO-FJSP. Then, our MAs are developed by incorporating a novel local search algorithm into the adapted NSGA-II, where some good individuals are chosen from the offspring population for local search using a selection mechanism. Furthermore, in the proposed local search, a hierarchical strategy is adopted to handle the three objectives, which mainly considers the minimization of makespan, while the concern of the other two objectives is reflected in the order of trying all the possible actions that could generate the acceptable neighbor. In the experimental studies, the influence of two alternative acceptance rules on the performance of the proposed MAs is first examined. Afterwards, the effectiveness of key components in our MAs is verified, including genetic search, local search, and the hierarchical strategy in local search. Finally, extensive comparisons are carried out with the state-of-the-art methods specially presented for the MO-FJSP on well-known benchmark instances. The results show that the proposed MAs perform much better than all the other algorithms.Note to Practitioners-The flexible job shop scheduling problem (FJSP) has important applications in textile, automobile assembly, semiconductor manufacturing, and many other industries. In the flexible job shop, a group of machines are capable for each operation, which is different from the traditional job shop environment where each operation can be processed by only a single machine. The FJSP is quite challenging, since the decisions include not only operation sequencing but also machine assignment. In the literature, the majority of studies for the FJSP are centered on optimizing the makespan. However, a single objective is deemed as insufficient for real and practical applications. Indeed, in the industry, production managers are usually concerned with more than one objective. This paper aims to simultaneously minimize the makespan, total workload, and critical workload for the FJSP, which can lead to both high throughput and load balance of machines. This problem is solved in the posterior approach, whose goal is to seek for a set of Pareto optimal solutions. We propose ef-fective memetic algorithms (MAs) that combine a classical multiobjective evolutionary technique referred as NSGA-II with a novel problem-specific local search. To enhance the ability to deal with multiple objectives, a hierarchical strategy is used in local search which gives varying degrees of consideration to each objective. The effectiveness of the proposed MAs is well demonstrated by extensive comparisons aga...

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Section: Memetic Algorithms For Multiobjective Combinatorial Optimmentioning

confidence: 99%

Multiobjective Flexible Job Shop Scheduling Using Memetic Algorithms

Yuan

2015

IEEE Trans. Automat. Sci. Eng.

222

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“…Figure 1a illustrates 240 Pareto optimal outcomes for this problem generated with a local search assisted evolutionary multiobjective optimization algorithm [31]. This figure is drawn to give an understanding of what the Pareto front looks like.…”

Section: The Three-objective Viennet's Test Problemmentioning

confidence: 99%

PAINT: Pareto front interpolation for nonlinear multiobjective optimization

2011

Self Cite

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A method called PAINT is introduced for computationally expensive multiobjective optimization problems. The method interpolates between a given set of Pareto optimal outcomes. The interpolation provided by the PAINT method implies a mixed integer linear surrogate problem for the original problem which can be optimized with any interactive method to make decisions concerning the original problem. When the scalarizations of the interactive method used do not introduce nonlinearity to the problem (which is true e.g., for the synchronous NIMBUS method), the scalarizations of the surrogate problem can be optimized with available mixed integer linear solvers. Thus, the use of the interactive method is fast with the surrogate problem even though the problem is computationally expensive. Numerical examples of applying the PAINT method for interpolation are included.

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“…Although this approach guarantees a local optimum with improved speed of convergence, the optimal switch timing is not known a priori on most practical problems. A recent study proposed a concurrent approach embedding a sequential quadratic programming (SQP) within NSGA-II (Sindhya et al 2011). In the concurrent approach, some or all of the intermediate solutions from the GA were regularly modified by the LSM during the process.…”

Section: Introductionmentioning

confidence: 99%

A Derivative-Free Hybrid Optimization Model for Short-Term Operation of a Multi-Objective Reservoir System Under Uncertainty

Chen

León

Chen

et al. 2018

Water Resour Manage

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Short-term operation of a multi-objective reservoir system under inflow uncertainty has been receiving increasing attention, however, major challenges for the optimization of this system still remain due to the multiple and often conflicting objectives, highly nonlinear constraints and uncertain parameters in which derivative information may not be directly available. Population-based optimization methods do not rely on derivatives while generally have a slow convergence. This study presents a hybrid optimization model for short-term operation of multi-objective reservoirs under uncertainty that is derivative free and has a relatively fast convergence. The model incorporates a local improvement method called Mesh Adaptive Direct Search (MADS) into a population-based method NSGA-II and has no requirement for differentiability, convexity and continuity of the optimization problem. The operation of a multi-objective and multi-reservoir system on the Columbia River under inflow uncertainty is used as a case study. Overall, the hybrid model outperforms optimization models based on either the NSGA-II only or the MADS only. The model is intended for conditions where derivative information of the optimization problem is unavailable, which could have a wide array of applications in water resources systems.

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Improving convergence of evolutionary multi-objective optimization with local search: a concurrent-hybrid algorithm

Cited by 34 publications

References 35 publications

Multiobjective Flexible Job Shop Scheduling Using Memetic Algorithms

Multiobjective Flexible Job Shop Scheduling Using Memetic Algorithms

PAINT: Pareto front interpolation for nonlinear multiobjective optimization

A Derivative-Free Hybrid Optimization Model for Short-Term Operation of a Multi-Objective Reservoir System Under Uncertainty

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