Despite their importance, hardly ever have multi-objective open shop problems been the topic of researches. This paper studies the mentioned problem and proposes some novel multi-objective solution methods centered on...
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Despite their importance, hardly ever have multi-objective open shop problems been the topic of researches. This paper studies the mentioned problem and proposes some novel multi-objective solution methods centered on the idea behind artificial immune and simulated annealing algorithms incorporating with powerful and fast local search engines. First, the algorithms are tuned and then carefully evaluated for their performance by means of multi-objective performance measures and statistical tools. An available ant colony optimization is also brought into the experiment. Among the proposed algorithms, the results show that the variant of enhanced artificial immune algorithm outperforms the others.
Division of the evolutionary search among multiple multi-objective evolutionary algorithms (MOEAs) is a recent advantage in MOEAs design, particularly in effective parallel and distributed MOEAs. However, most these a...
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Division of the evolutionary search among multiple multi-objective evolutionary algorithms (MOEAs) is a recent advantage in MOEAs design, particularly in effective parallel and distributed MOEAs. However, most these algorithms rely on such a central (re) division that affects the algorithms' efficiency. This paper first proposes a local MOEA that searches on a particular region of objective space with its novel evolutionary selections. It effectively searches for Pareto Fronts (PFs) inside the given polar-based region, while nearby the region is also explored, intelligently. The algorithm is deliberately designed to adjust its search direction to outside the region - but nearby - in the case of a region with no Pareto Front. With this contribution, a novel island model is proposed to run multiple forms of the local MOEA to improve a conventional MOEA (e. g. NSGA-II or MOEA/D) running along - in another island. To dividing the search, a new division technique is designed to give particular regions of objective space to the local MOEAs, frequently and effectively. Meanwhile, the islands benefit from a sophisticated immigration strategy without any central (re) collection, (re) division and (re) distribution acts. Results of three experiments have confirmed that the proposed island model mostly outperforms to the clustering MOEAs with similar division technique and similar island models on DTLZs. The model is also used and evaluated on a real-world combinational problem, flexible logistic network design problem. The model definitely outperforms to a similar island model with conventional MOEA (NSGA-II) used in each island. (C) 2012 Elsevier B. V. All rights reserved.
Most multi-objective optimization problems (MOPs) have a set of optimal trade-off solutions known as the Pareto-optimal solutions since the objectives in MOPs are usually in conflict with one another. Recently propose...
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Most multi-objective optimization problems (MOPs) have a set of optimal trade-off solutions known as the Pareto-optimal solutions since the objectives in MOPs are usually in conflict with one another. Recently proposed estimation of distribution algorithms (EDAs) build a probability distribution model based on the probabilistic information about decision variables of solutions, and then produce new solutions from the model. In the algorithms, the modeling technique enables the initial large search space to be reduced to small promising solution space during the search. However, the existing EDAs might be inefficient at generating the promising solutions since they depend on the information extracted from the decision variables of current solutions expected to approach the optimal solutions. For effective modeling of the promising solutions, we firstly introduce new information about the relationship between decision variables and objective functions;this information is called sensitivity of objective function. Secondly, we propose a multi-objective estimation of distribution algorithm based on the sensitivity of objective function (MOEDA-S). In the MOEDA-S, the sensitivity-based distribution modeling adapts to the current search strategy such that the convergence-focused search at the beginning part of the search is changed to a diversity-focused search at the latter part of the search. MOEDA-S is compared with two other leading multi-objective evolutionary algorithms on a set of test instances. The simulation results show that MOEDA-S outperforms the two compared algorithms in terms of both convergence and diversity performances of the solutions.
作者：
Zhu, ChenLiu, JingXidian Univ
Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China
A direction based multi-objective agent genetic algorithm (DMOAGA) is proposed in this paper. In order to take advantage of the effective direction information and depth of local search to mine non-dominated solutions...
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ISBN:
(纸本)9783642412783;9783642412776
A direction based multi-objective agent genetic algorithm (DMOAGA) is proposed in this paper. In order to take advantage of the effective direction information and depth of local search to mine non-dominated solutions, the direction perturbation operator is also employed. The neighborhood non-dominated solutions are generated using tournament selection and "average distance" rule, which maintains the diversity of non-dominated solution set. In the experiments, the benchmark problems UF1 similar to UF6 and ZDT1 similar to ZDT4 are used to validate the performance of DMOAGA. We compared it with NSGA-II and DMEA in terms of generational distance (GD) and inverted generational distance (IGD). The results show that DMOAGA has a good diversity and convergence, the performances on most of benchmark problems are better than DMEA and NSGA-II.
By replacing the selection component, a well researched evolutionary algorithm for scalar optimizationproblems (SOPs) can be directly used to solve multi-objective optimization problems (MOPs). Therefore, in most of ...
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By replacing the selection component, a well researched evolutionary algorithm for scalar optimizationproblems (SOPs) can be directly used to solve multi-objective optimization problems (MOPs). Therefore, in most of existing multi-objective evolutionary algorithms (MOEAs), selection and diversity maintenance have attracted a lot of research effort. However, conventional reproduction operators designed for SOPs might not be suitable for MOPs due to the different optima structures between them. At present, few works have been done to improve the searching efficiency of MOEAs according to the characteristic of MOPs. Based on the regularity of continues MOPs, a Baldwinian learning strategy is designed for improving the nondominated neighbor immune algorithm and a multi-objective immune algorithm with Baldwinian learning (MIAB) is proposed in this study. The Baldwinian learning strategy extracts the evolving environment of current population by building a probability distribution model and generates a predictive improving direction by combining the environment information and the evolving history of the parent individual. Experimental results based on ten representative benchmark problems indicate that, MIAB outperforms the original immune algorithm, it performs better or similarly the other two outstanding approached NSGAII and MOEA/D in solution quality on most of the eight testing MOPs. The efficiency of the proposed Baldwinian learning strategy has also been experimentally investigated in this work. (C) 2012 Elsevier B. V. All rights reserved.
There exist two general approaches to solve multiple objectiveproblems. The first approach involves the aggregation of all the objective functions into a single composite objective function. Mathematical methods such...
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There exist two general approaches to solve multiple objectiveproblems. The first approach involves the aggregation of all the objective functions into a single composite objective function. Mathematical methods such as the weighted sum method, goal programming, or utility functions are methods that pertain to this general approach. The output of this method is a single solution. On the other hand, we have the multiple objective evolutionary algorithms that offer the decision maker a set of trade off solutions usually called non dominated solutions or, Pareto-optimal solutions. This set is usually very large and the decision maker faces the problem of reducing the size of this set to have a manageable number of solutions to analyze. This paper presents a post- Pareto approach to prune the non-dominated set of solutions obtained by multiple objective evolutionary algorithms. The proposed approach uses a non-uniform weight generator method to reduce the size of the Pareto-optimal set. A pair of examples is presented to show the performance of the method.
This paper deals with Smart Grids including renewable generation plants and plug-in vehicle fleets managed by aggregators and connected to the grid through power electronic devices. A new multi-objectiveoptimization ...
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ISBN:
(纸本)9781467312998
This paper deals with Smart Grids including renewable generation plants and plug-in vehicle fleets managed by aggregators and connected to the grid through power electronic devices. A new multi-objectiveoptimization model is proposed in order to obtain the optimal operation of the Smart Grids. The proposed model originates from the need of accomplishing numerous and sometime conflicting objectives in the Smart Grid operation and allows to perform several services, aimed at contemporaneously meeting needs internal to the SG and external to it. Among the possible optimization methods to solve the multi-objective model, two different approaches were used in this paper based on objective sum criterion and weighted sum criterion, respectively. The objective sum criterion does not require any articulation of preferences and seems particularly suitable when low CPU time is required. The weighted sum criterion could lead to better quality of decisions since it evaluates the relative importance of each objective. Numerical simulations applying the analyzed methods for the optimal operation of a low voltage test Smart Grid were performed and discussed.
Distribution system planners have frequently to choose among different solutions either in deterministic framework or under problem data uncertainty. In these conditions, adequate methods are needed to choose the opti...
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ISBN:
(纸本)9781627483889
Distribution system planners have frequently to choose among different solutions either in deterministic framework or under problem data uncertainty. In these conditions, adequate methods are needed to choose the optimal solution. This paper refers to electrical distribution systems with static converters and formulates the planning problem of passive filtering systems as a multi-objectiveoptimization problem. A heuristic simplified approach including trade-off/risk analysis issues is proposed to solve the problem with the aim of optimizing several objectives and meeting proper equality and inequality constraints. The approach is able to quickly find solutions on the Pareto frontier that can help the Decision Maker in selecting the final planning alternative to be practically operated. Both deterministic and uncertain frameworks are taken into account. This paper is a companion paper to a paper of the same title, Part II [1], in which the proposed approach is applied on a 17-busbars distribution test system.
This paper presents a mathematical model of trade-off relations arising in third party logistics using Pareto optimal solutions for multi-objective optimization problems. The model defines an optimal set of distributi...
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This paper presents a mathematical model of trade-off relations arising in third party logistics using Pareto optimal solutions for multi-objective optimization problems. The model defines an optimal set of distribution costs and service levels constituting a trade-off relation. An analogy to the concept of the indifference curve in the field of economics is discussed. Numerical experiments for a simplified problem are performed, demonstrating an increasing process of the utility of logistics. (C) 2008 Elsevier B.V. All rights reserved.
This paper presents an algorithm for thermal optimization formulation strategies for multi-heat generation of integrated circuit (IC) on printed circuit board (PCB). Weighted-sum approach for multi-objective genetic a...
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ISBN:
(纸本)9781424445462
This paper presents an algorithm for thermal optimization formulation strategies for multi-heat generation of integrated circuit (IC) on printed circuit board (PCB). Weighted-sum approach for multi-objective genetic algorithm (WMOGA) with formulated initial placement and multi-constraints parameters (FIPMCP) are presented. FIPMCP is used for the components selection and components to PCB placement mapping procedures for random initial population. The objectives are to optimize thermal distribution f(T) of electronic components on PCB and the PCB area f(A) needed simultaneously. For multi-objectiveoptimization process, non-dominated optimal solutions and the best fitness WMOGA over iterations are plotted for both cases in order to obtain the best PCB optimal design solution. The results show that the best solution of f(T),f(A) and F(T,A) are minimized by 1.80%, 8.54% and 4.97% respectively.
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