Multi-objective Memetic Optimization Research Articles

Thematic issue on advances in analysis and application of multi-objective memetic optimization algorithms

Thematic issue on advances in analysis and application of multi-objective memetic optimization algorithms

Optimal Colour Image Watermarking Using Neural Networks and Multiobjective Memetic Optimization

This paper deals with the problem of robust and perceptual logo watermarking for colour images. In particular, we investigate trade-off factors in designing efficient watermarking techniques to maximize the quality of watermarked images and the robustness of watermark. With the fixed size of a logo watermark, there is a conflict between these two objectives, thus a multiobjective optimization problem is introduced. We propose to use a hybrid between general regression neural networks (GRNNs) and multiobjective memetic algorithms (MOMA) to solve this challenging problem. Specifically, a GRNN is used for efficient watermark embedding and extraction in the wavelet domain. Optimal watermark embedding factors and the smooth parameter of the GRNN are searched by a MOMA for optimally embedding watermark bits into wavelet coefficients. The experimental results show that the proposed approach achieves robustness and imperceptibility in watermarking.

Sustainable scheduling of distributed permutation flow-shop with non-identical factory using a knowledge-based multi-objective memetic optimization algorithm

Abstract With the development of economic globalization and sustainable manufacturing, sustainable scheduling of distributed manufacturing has attracted increasing concern. However, distributed manufacturing with the non-identical factory is rarely researched. Meanwhile, scheduling can affect the sustainability of manufacturing. Thus, this paper is the first attempt to investigate a sustainable distributed permutation flow-shop scheduling problem with a non-identical factory (DPFSP-NF). We formulate a novel mathematical model of this DPFSP-NF with objectives of minimizing makespan, negative social impact (NSI), and total energy consumption (TEC). A knowledge-based multi-objective memetic optimization algorithm (KMMOA) is presented to address this DPFSP-NF. First, a new energy conservation strategy is designed and embedded in the model to reduce TEC criterion. Second, a cooperative initialization mechanism is presented to yield initial solutions with good diversity and convergence. Third, several properties of DPFSP-NF are investigated and utilized to develop the knowledge-based local search operator. The impact of parameter configuration on KMMOA is studied by the Taguchi method. Finally, we compare KMMOA to its variants and other well-known multi-objective optimization algorithms by performing a number of experiments. Experiment results demonstrate the effectiveness of each improvement component of the KMMOA, and verify that KMMOA is an effective approach to deal with the DPFSP-NF.

Threshold Based Multi-Objective Memetic Optimized Round Robin Scheduling for Resource Efficient Load Balancing in Cloud

Task scheduling is a significant problem to be resolved for balancing the workload on a cloud server. One of the key problems that affect the scheduling performance is burstiness workloads. Few research studies have been introduced to schedule tasks and balancing loads in the cloud. However, scheduling performance of existing technique was not effective in burstiness workload’s conditions. Thus, there is a need for a novel task scheduling technique to handle bursty user demands and provide high-quality cloud services. Therefore, Threshold Based Multi-Objective Memetic Optimized Round Robin Scheduling (T-MMORRS) Technique is proposed in this research work. At first, user requests are sent to the cloud server. After that, T-MMORRS Technique employs burst detector to determine the workload condition as normal or that which is bursty. Based on burst detector result, then T-MMORRS Technique adapts the two different load balancing algorithms for efficiently scheduling the user tasks. The T-MMORRS Technique chooses Threshold Multi-Objective Memetic Optimization (TMMO) in normal workload situation and Weighted Multi-Objective Memetic Optimized Round Robin Scheduling (WMMORRS) in burstiness workload state. Finally, the selected load balancing algorithm in MMORRS Technique schedules the user request task to a resource-efficient virtual machine with higher efficiency and lower time consumption. As a result, T-MMORRS Technique enhances the task scheduling performance to balance the both bursty and non-bursty workloads of virtual machines in the cloud. The experimental evaluation of T-MMORRS Technique is conducted using factors such as scheduling efficiency, scheduling time and energy consumption with respect to the number of user requests. The experimental result shows that the T-MMORRS Technique can enhance the scheduling efficiency and also minimizes the energy usage in the cloud as compared to state-of-the-art works.

Multi-objective memetic optimization for the bi-objective obnoxious p-median problem

Location problems have been studied extensively in the optimization literature, the p-median being probably one of the most tackled models. The obnoxious p-median is an interesting variant that appears in the context of hazardous location. The aim of this paper is to formally introduce a bi-objective optimization model for this problem, in which a solution consists of a set of p locations, and two conflicting objectives arise. On the one hand, the sum of the minimum distance between each client and their nearest open facility and, on the other hand, the dispersion among facilities. Both objective values should be kept as large as possible for a convenient location of dangerous facilities.We propose a Multi-Objective Memetic Algorithm (MOMA) to obtain high-quality approximations to the efficient front of the bi-objective obnoxious p-median problem, denoted as Bi-OpM. In particular, we introduce efficient crossover and mutation mechanisms. Additionally, we present several multi-objective local search methods. All the strategies are finally incorporated in a memetic algorithm which limits the search to the feasible region, thus performing an efficient exploration of the solutions space. Our experimentation compares several state-of-the-art procedures with the introduced MOMA emerging as the best performing method in all considered multi-objective metrics.

Adaptive Multiobjective Memetic Optimization

Multiobjective memetic optimization algorithms (MMOAs) are recently applied to solve nonlinear optimization problems with conflicting objectives. An important issue in an MMOA is how to identify the relative best solutions to guide its adaptive processes. In this paper, the authors introduce a framework of adaptive multiobjective memetic optimization algorithms (AMMOA) with an information theoretic criterion for guiding the adaptive selection, clustering, local learning processes, and a robust stopping criterion of AMMOA. The implementation of AMMOA is applied to several benchmark test problems with remarkable results. The paper also presents the application of AMMOA in designing an optimal image watermarking to maximize the quality of the watermarked images and the robustness of the watermark.

Green transportation scheduling with pickup time and transport mode selections using a novel multi-objective memetic optimization approach

This paper addresses a green transportation scheduling problem with realistic constraints widely existing in make-to-order manufacturing supply chains, such as pickup time and transport mode selections. The mathematical model of this problem is presented, which is formulated as a bi-objective mixed integer nonlinear program. The problem is simplified first by converting this program to a bi-objective integer nonlinear program. A novel evolution-strategy-based memetic Pareto optimization (ESMPO) approach is then developed to handle this new program, in which a multi-objective local search process is proposed to seek promising neighboring individuals and the faster nondominated sorting procedure is introduced into the memetic algorithm to perform multi-objective sorting. The performance of the proposed ESMPO approach is evaluated by numerical experiments based on industrial data and industrial-sized problems. Experimental results demonstrate that the proposed approach can effectively solve the investigated problem by generating much better solutions than 3 other metaheuristic-based Pareto optimization approaches and the industrial method do.

A hybrid intelligent model for order allocation planning in make-to-order manufacturing

This paper investigated a multi-objective order allocation planning problem in make-to-order manufacturing with the consideration of various real-world production features. A novel hybrid intelligent optimization model, integrating a multi-objective memetic optimization (MOMO) process, a Monte Carlo simulation technique and a heuristic pruning technique, is developed to tackle this problem. The MOMO process, combining a NSGA-II optimization process with a tabu search, is proposed to provide Pareto optimal solutions. Extensive experiments based on industrial data are conducted to validate the proposed model. Results show that (1) the proposed model can effectively solve the investigated problem by providing effective production decision-making solutions; (2) the MOMO process has better capability of seeking global optimum than an NSGA-II-based optimization process and an industrial method.