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53
DEMO: Differential Evolution for multiobjective optimization
- In Proceedings of the 3rd International Conference on Evolutionary MultiCriterion Optimization (EMO 2005
, 2005
"... Abstract. Differential Evolution (DE) is a simple but powerful evolutionary optimization algorithm with many successful applications. In this paper we propose Differential Evolution for Multiobjective Optimization (DEMO) – a new approach to multiobjective optimization based on DE. DEMO combines the ..."
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Abstract. Differential Evolution (DE) is a simple but powerful evolutionary optimization algorithm with many successful applications. In this paper we propose Differential Evolution for Multiobjective Optimization (DEMO) – a new approach to multiobjective optimization based on DE. DEMO combines the advantages of DE with the mechanisms of Paretobased ranking and crowding distance sorting, used by state-of-the-art evolutionary algorithms for multiobjective optimization. DEMO is implemented in three variants that achieve competitive results on five ZDT test problems. 1
Solving rotated multi-objective optimization problems using Differential Evolution
- In AI 2004: Advances in Artificial Intelligence: 17th Australian Joint Conference on Artificial Intelligence
, 2004
"... Abstract. This paper demonstrates that the self-adaptive technique of Differential Evolution (DE) can be simply used for solving a multiobjective optimization problem where parameters are interdependent. The real-coded crossover and mutation rates within the NSGA-II have been replaced with a simple ..."
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Cited by 41 (4 self)
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Abstract. This paper demonstrates that the self-adaptive technique of Differential Evolution (DE) can be simply used for solving a multiobjective optimization problem where parameters are interdependent. The real-coded crossover and mutation rates within the NSGA-II have been replaced with a simple Differential Evolution scheme, and results are reported on a rotated problem which has presented difficulties using existing Multi-objective Genetic Algorithms. The Differential Evolution variant of the NSGA-II has demonstrated rotational invariance and superior performance over the NSGA-II on this problem. 1
A Simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution Algorithm
, 2004
"... Differential Evolution (DE) algorithm is a new heuristic approach mainly having three advantages; finding the true global minimum regardless of the initial parameter values, fast convergence, and using few control parameters. DE algorithm is a population based algorithm like genetic algorithms using ..."
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Cited by 28 (0 self)
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Differential Evolution (DE) algorithm is a new heuristic approach mainly having three advantages; finding the true global minimum regardless of the initial parameter values, fast convergence, and using few control parameters. DE algorithm is a population based algorithm like genetic algorithms using similar operators; crossover, mutation and selection. In this work, we have compared the performance of DE algorithm to that of some other well known versions of genetic algorithms: PGA, Grefensstette, Eshelman. In simulation studies, De Jong’s test functions have been used. From the simulation results, it was observed that the convergence speed of DE is significantly better than genetic algorithms. Therefore, DE algorithm seems to be a promising approach for engineering optimization problems.
Pareto-based Multi-Objective Differential Evolution
, 2003
"... Evolutionary multi-objective optimization (EMOO) finds a set of Pareto solutions rather than any single aggregated optimal solution for a multi-objective problem. The purpose of this paper is to describe a newly developed evolutionary approach -- Pareto-based multi-objective differential evolution ..."
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Cited by 26 (0 self)
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Evolutionary multi-objective optimization (EMOO) finds a set of Pareto solutions rather than any single aggregated optimal solution for a multi-objective problem. The purpose of this paper is to describe a newly developed evolutionary approach -- Pareto-based multi-objective differential evolution (MODE). In this paper, the concept of differential evolution, which is well-known in the continuous single-objective domain for its fast convergence and adaptive parameter setting, is extended to the multi-objective problem domain. A Pareto-based approach is proposed to implement the differential vectors. A set of benchmark test functions is used to validate this new approach. We compare the computational results with those obtained in the literature, specifically by strength Pareto evolutionary algorithm (SPEA). It is shown that this new approach tends to be more effective in finding the Pareto front in the sense of accuracy and approximate representation of the real Pareto front with comparable efficiency.
Multi-objective Differential Evolution (MODE) for optimization of Adiabatic Styrene
- Reactor,” Chemical Engineering Science
, 2005
"... Abstract — Many problems in the engineering domain involve more than one objective to be optimized simultaneously. The optimal solution to a multi-objective function results in a set of equally good solutions (Pareto optimal set), rather than a unique solution. Several entities are present in a typi ..."
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Cited by 17 (4 self)
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Abstract — Many problems in the engineering domain involve more than one objective to be optimized simultaneously. The optimal solution to a multi-objective function results in a set of equally good solutions (Pareto optimal set), rather than a unique solution. Several entities are present in a typical supply chain problem. Each of these entities has its individual objectives. When all the objectives of supply chain are combined they work towards a common goal of increasing the profitability of an organization. The supply chain model is thus multi-objective in nature which involves several conflicting objectives. A three-stage supply chain problem (involving a network of supplier, plant and customer zones) is solved using Multi-Objective Differential Evolution (MODE) algorithm in this work. Three cases of objective functions are considered in this study. Pareto optimal solutions are obtained for each case. The results are compared with those reported using NSGA-II in the literature. I.
ExPERT: Pareto-Efficient Task Replication on Grids and a Cloud
"... Abstract—Many scientists perform extensive computations by executing large bags of similar tasks (BoTs) in mixtures of computational environments, such as grids and clouds. Although the reliability and cost may vary considerably across these environments, no tool exists to assist scientists in the s ..."
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Abstract—Many scientists perform extensive computations by executing large bags of similar tasks (BoTs) in mixtures of computational environments, such as grids and clouds. Although the reliability and cost may vary considerably across these environments, no tool exists to assist scientists in the selection of environments that can both fulfill deadlines and fit budgets. To address this situation, we introduce the ExPERT BoT scheduling framework. Our framework systematically selects from a large search space the Pareto-efficient scheduling strategies, that is, the strategies that deliver the best results for both makespan and cost. ExPERT chooses from them the best strategy according to a general, user-specified utility function. Through simulations and experiments in real production environments, we demonstrate that ExPERT can substantially reduce both makespan and cost in comparison to common scheduling strategies. For bioinformatics BoTs executed in a real mixed grid+cloud environment, we show how the scheduling strategy selected by ExPERT reduces both makespan and cost by 30%-70%, in comparison to commonlyused scheduling strategies. Keywords—bags-of-tasks; cloud; grid; Pareto-frontier I.
Incorporating directional information within a differential evolution algorithm for multiobjective optimization
- in Proceedings of the 2006 Genetic and Evolutionary Computation Conference (GECCO-06
, 2006
"... The field of Differential Evolution (DE) has demonstrated important advantages in single objective optimization. To date, no previous research has explored how the unique characteristics of DE can be applied to multi-objective optimization. This paper explains and demonstrates how DE can provide adv ..."
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Cited by 8 (4 self)
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The field of Differential Evolution (DE) has demonstrated important advantages in single objective optimization. To date, no previous research has explored how the unique characteristics of DE can be applied to multi-objective optimization. This paper explains and demonstrates how DE can provide advantages in multi-objective optimization using directional information. We present three novel DE variants for multi-objective optimization, and a report of their performance on four multi-objective problems with different characteristics. The DE variants are compared with the NSGA-II (Non-dominated Sorting Genetic Algorithm). The results suggest that directional information yields improvements in convergence speed and spread of solutions.
The good of the many outweighs the good of the one: evolutionary multi-objective optimization
- IEEE Connections Newsletter
, 2003
"... Abstract. We dwell in largely non-technical terms on the essential differences between single-objective optimization and multiple-objective optimization. We argue in particular that single-objective approaches to real-world problems are almost invariably simplifications of the real-problem which mak ..."
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Cited by 8 (1 self)
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Abstract. We dwell in largely non-technical terms on the essential differences between single-objective optimization and multiple-objective optimization. We argue in particular that single-objective approaches to real-world problems are almost invariably simplifications of the real-problem which make many ideal solutions unreachable to the optimization method. We promote the use of multi-objective optimization methods, particularly those arising from the evolutionary computation community. We point out that the state of the art in the field of evolutionary multi-objective optimization is such that fast and effective techniques are now available which are capable of finding a well-distributed set of diverse trade-off solutions, with little or no more computational effort than sophisticated single-objective optimizers would have taken to find a single one. The resulting diversity of ideas available through a multi-objective approach leads both to the problem-solver being furnished with a better understanding of the space of possible solutions, and consequently to a better final solution to the problem at hand. We end by very briefly charting the history of the field and hinting at the range of published applications and ongoing research issues. 1
Choosing Leaders for Multi-objective PSO Algorithms Using Differential Evolution
"... Abstract. The fast convergence of particle swarm algorithms can become a downside in multi-objective optimization problems when there are many local optimal fronts. In such a situation a multi-objective particle swarm algorithm may get stuck to a local Pareto optimal front. In this paper we propose ..."
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Cited by 8 (3 self)
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Abstract. The fast convergence of particle swarm algorithms can become a downside in multi-objective optimization problems when there are many local optimal fronts. In such a situation a multi-objective particle swarm algorithm may get stuck to a local Pareto optimal front. In this paper we propose a new approach in selecting leaders for the particles to follow, which in-turn will guide the algorithm towards the Pareto optimal front. The proposed algorithm uses a Differential Evolution operator to create the leaders. These leaders can successfully guide the other particles towards the Pareto optimal front for various types of test problems. This simple yet robust algorithm is effective compared with existing multi-objective particle swarm algorithms.
