作者：
Du, YanlianFeng, ZejingShen, YijunHainan Univ
Sch Informat & Commun Engn Haikou 570228 Hainan Peoples R China Hainan Univ
State Key Lab Marine Resources Utilizat South Chi Haikou 570228 Hainan Peoples R China Hainan Univ
Coll Appl Sci & Technol Danzhou 571737 Peoples R China
Nondominated-sorting plays an important role in multi-objective evolutionary algorithm in recent decades. However, it fails to work well when the target multi-objectiveproblem has a complex Pareto front, brusque nond...
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ISBN:
(纸本)9783031138706;9783031138690
Nondominated-sorting plays an important role in multi-objective evolutionary algorithm in recent decades. However, it fails to work well when the target multi-objectiveproblem has a complex Pareto front, brusque nondominated-sorting virtually steers by the conflicting nature of objectives, which leads to irrationality. In this paper, a novel mixed-factor evolutionary algorithm is proposed. A normalization procedure, i.e. mixed-factor, is introduced in the objective space, which links all the objectives for all the solutions of the problem to ease the conflicting nature. In the process of nondominated-sorting, the mixed factors of individual substitute the raw objectives. In order to ensure that the population are thoroughly steered through the normalized objective space, hybrid ageing operator and static hypermutation with first constructive mutation are used to guide the searching agents converge towards the true Pareto front. The algorithm proposed is operated on multi-objective knapsack problem. The effectiveness of MFEA is compared with five state-of-the-art algorithms, i.e., NSGA-II, NSGA-III, MOEA/D, SPEA2 and GrEA, in terms of five performance metrics. Simulation results demonstrate that MFEA achieves better performance.
The use of metaheuristics to solve multi-objective optimization problems (MOP) is a very active research topic. Ant Colony Optimization (ACO) has received a growing interest in the last years for such problems. Many a...
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The use of metaheuristics to solve multi-objective optimization problems (MOP) is a very active research topic. Ant Colony Optimization (ACO) has received a growing interest in the last years for such problems. Many algorithms have been proposed in the literature to solve different MOP. This paper presents an indicator-based ant colony optimization algorithm called IBACO for the multi-objective knapsack problem (MOKP). The IBACO algorithm proposes a new idea that uses binary quality indicators to guide the search of artificial ants. These indicators were initially used by Zitzler and Kunzli in in the selection process of their evolutionary algorithm IBEA. In this paper, we use the indicator optimization principle to reinforce the best solutions by rewarding pheromone trails. We carry out a set of experiments on MOKP benchmark instances by applying the two binary indicators: epsilon indicator and hypervolume indicator. The comparison of the proposed algorithm with IBEA, ACO and other state-of-the-art evolutionary algorithms shows that IBACO is significantly better on most instances. (C) 2015 The Authors. Published by Elsevier B.V.
The use of metaheuristics to solve multi-objective optimization problems (MOP) is a very active research topic. Ant Colony Optimization (ACO) has received a growing interest in the last years for such problems. Many a...
详细信息
The use of metaheuristics to solve multi-objective optimization problems (MOP) is a very active research topic. Ant Colony Optimization (ACO) has received a growing interest in the last years for such problems. Many algorithms have been proposed in the literature to solve different MOP. This paper presents an indicator-based ant colony optimization algorithm called IBACO for the multi-objective knapsack problem (MOKP). The IBACO algorithm proposes a new idea that uses binary quality indicators to guide the search of artificial ants. These indicators were initially used by Zitzler and Künzli in the selection process of their evolutionary algorithm IBEA. In this paper, we use the indicator optimization principle to reinforce the best solutions by rewarding pheromone trails. We carry out a set of experiments on MOKP benchmark instances by applying the two binary indicators: epsilon indicator and hypervolume indicator. The comparison of the proposed algorithm with IBEA, ACO and other state-of-the-art evolutionary algorithms shows that IBACO is significantly better on most instances.
We consider multi-objective optimization problems where the decision maker (DM) has equity concerns. We assume that the preference model of the DM satisfies properties related to inequity-aversion, hence we focus on f...
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We consider multi-objective optimization problems where the decision maker (DM) has equity concerns. We assume that the preference model of the DM satisfies properties related to inequity-aversion, hence we focus on finding nondominated solutions in line with the properties of inequity-averse preferences, namely the equitably nondominated solutions. We discuss two algorithms for finding good subsets of equitably nondominated solutions. The first approach is an extension of an interactive approach developed for finding the most preferred nondominated solution when the utility function is assumed to be quasiconcave. We find the most preferred equitably nondominated solution when the utility function is assumed to be symmetric quasiconcave. In the second approach we generate an evenly distributed subset of the set of equitably nondominated solutions to be considered further by the DM. We show the computational feasibility of the two algorithms on equitable multi-objective knapsack problem, in which projects in different categories are to be funded subject to a limited budget. We perform experiments to show and discuss the performances of the algorithms.
Motivated by the success of matheuristics in the single-objective domain, we propose a very simple linear programming-based matheuristic for three-objective binary integer programming. To tackle the problem, we obtain...
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ISBN:
(纸本)9789897584855
Motivated by the success of matheuristics in the single-objective domain, we propose a very simple linear programming-based matheuristic for three-objective binary integer programming. To tackle the problem, we obtain lower bound sets by means of the vector linear programming solver Bensolve. Then, simple heuristic approaches, such as rounding and path relinking, are applied to this lower bound set to obtain high-quality approximations of the optimal set of trade-off solutions. The proposed algorithm is compared to a recently suggested algorithm which is, to the best of our knowledge, the only existing matheuristic method for threeobjective integer programming. Computational experiments show that our method produces a better approximation of the true Pareto front using significantly less time than the benchmark method on standard benchmark instances for the three-objectiveknapsackproblem.
In the present work, we are interested in the practical behavior of a new fully polynomial time approximation schemes (fptas) to solve the approximation version of the 0-1 multi-objective knapsack problem. The propose...
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In the present work, we are interested in the practical behavior of a new fully polynomial time approximation schemes (fptas) to solve the approximation version of the 0-1 multi-objective knapsack problem. The proposed methodology makes use of very general techniques (such as dominance relations in dynamic programming) and thus may be applicable in the implementation of fptas for other problems as well. Extensive numerical experiments on various types of instances in the bi and tri-objective cases establish that our method performs very well both in terms of CPU time and size of solved instances. We point out some reasons for the good practical performance of our algorithm. A comparison with an exact method and the fptas proposed in [Erlebach, T., Kellerer, H., Pferschy, U., 2002. Approximating multi-objective knapsack problems. Management Science 48 (12), 1603-1612] is also performed. (C) 2008 Elsevier B.V. All rights reserved,
In this paper, we present an approach, based on dynamic programming, for solving the 0-1 multi-objective knapsack problem. The main idea of the approach relies on the use of several complementary dominance relations t...
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In this paper, we present an approach, based on dynamic programming, for solving the 0-1 multi-objective knapsack problem. The main idea of the approach relies on the use of several complementary dominance relations to discard partial solutions that cannot lead to new non-dominated criterion vectors. This way, we obtain an efficient method that outperforms the existing methods both in terms of CPU time and size of solved instances. Extensive numerical experiments on various types of instances are reported. A comparison with other exact methods is also performed. In addition, for the first time to our knowledge, we present experiments in the three-objective case. (C) 2007 Elsevier Ltd. All rights reserved.
This paper presents an improved multi-objective quantum-inspired evolutionary algorithm (IMQEA) for solving multi-objective optimization problems (MOPs). Different from general MQEAs, the proposed approach uses multip...
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ISBN:
(纸本)9781467317146
This paper presents an improved multi-objective quantum-inspired evolutionary algorithm (IMQEA) for solving multi-objective optimization problems (MOPs). Different from general MQEAs, the proposed approach uses multiple observations to yield candidate solutions. In the early stage of evolution, multiple observations of a given quantum bit (Q-bit) individual can yield solutions with good diversity, which is helpful for global search. In the later stage, most Q-bits have matured, and thus multiple observations of a given Q-bit individual are similar to a local search, which improves the accuracy of solutions. Experimental results for the multi-objective 0/1 knapsackproblem show that the IMQEA finds solutions close to the Pareto-optimal front and maintains a good spread of the non-dominated set.
In this paper, we present an approach, based on dynamic programming, for solving 0-1 multi-objective knapsack problems. The main idea of the approach relies on the use of several complementary dominance relations to d...
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ISBN:
(纸本)9783540728443
In this paper, we present an approach, based on dynamic programming, for solving 0-1 multi-objective knapsack problems. The main idea of the approach relies on the use of several complementary dominance relations to discard partial solutions that cannot lead to new non-dominated criterion vectors. This way, we obtain an efficient method that outperforms the existing methods both in terms of CPU time and size of solved instances. Extensive numerical experiments on various types of instances are reported. A comparison with other exact methods is also performed. In addition, for the first time to our knowledge, we present experiments in the three-objective case.
In the present work, we are interested in the practical behavior of a new fptas to solve the approximation version of the 0-1 multiobjectiveknapsackproblem. Nevertheless, our methodology focuses on very general tech...
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ISBN:
(纸本)9783540755197
In the present work, we are interested in the practical behavior of a new fptas to solve the approximation version of the 0-1 multiobjectiveknapsackproblem. Nevertheless, our methodology focuses on very general techniques (such as dominance relations in dynamic programming) and thus may be applicable in the implementation of fptas for other problems as well. Extensive numerical experiments on various types of instances establish that our method performs very well both in terms of CPU time and size of solved instances. We point out some reasons for the good practical performance of our algorithm. A comparison with an exact method is also performed.
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