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22
A multiobjective evolutionary algorithm based on decomposition
 IEEE Transactions on Evolutionary Computation, Accepted
, 2007
"... 1 Decomposition is a basic strategy in traditional multiobjective optimization. However, this strategy has not yet widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It decomposes a MOP into a number o ..."
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Cited by 44 (14 self)
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1 Decomposition is a basic strategy in traditional multiobjective optimization. However, this strategy has not yet widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It decomposes a MOP into a number of scalar optimization subproblems and optimizes them simultaneously. Each subproblem is optimized by using information from its several neighboring subproblems, which makes MOEA/D have lower computational complexity at each generation than MOGLS and NSGAII. Experimental results show that it outperforms or performs similarly to MOGLS and NSGAII on multiobjective 01 knapsack problems and continuous multiobjective optimization problems. Index Terms multiobjective optimization, decomposition, evolutionary algorithms, memetic algorithms, Pareto optimality, computational complexity. I.
The Automatic Design of MultiObjective Ant Colony Optimization Algorithms
, 2011
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An Analysis of Algorithmic Components for Multiobjective Ant Colony Optimization: A Case . . .
, 2009
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C.: Strong combination of ant colony optimization with constraint programming optimization
 In: Proceedings of the 7th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
, 2010
"... Abstract. We introduce an approach which combines ACO (Ant Colony ..."
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Cited by 2 (0 self)
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Abstract. We introduce an approach which combines ACO (Ant Colony
PopulationACO for the Automotive Deployment Problem Evolutionary Multiobjective Optimisation
"... The automotive deployment problem is a realworld constrained multiobjective assignment problem in which software components must be allocated to processing units distributed around a car’s chassis. Prior work has shown that evolutionary algorithms such as NSGAII can produce good quality solutions ..."
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Cited by 2 (2 self)
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The automotive deployment problem is a realworld constrained multiobjective assignment problem in which software components must be allocated to processing units distributed around a car’s chassis. Prior work has shown that evolutionary algorithms such as NSGAII can produce good quality solutions to this problem. This paper presents a populationbased ant colony optimisation (PACO) approach that uses a single pheromone memory structure and a range of local search operators. The PACO and prior NSGAII are compared on two realistic problem instances. Results indicate that the PACO is generally competitive with NSGAII and performs more effectively as problem complexity—size and number of objectives—is increased.
ANT COLONY OPTIMIZATION BASED SIMULATION OF 3D AUTOMATIC HOSE/PIPE ROUTING
, 2009
"... This thesis focuses on applying one of the rapidly growing nondeterministic optimization algorithms, the ant colony algorithm, for simulating automatic hose/pipe routing with several conflicting objectives. Within the thesis, methods have been developed and applied to single objective hose routing, ..."
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This thesis focuses on applying one of the rapidly growing nondeterministic optimization algorithms, the ant colony algorithm, for simulating automatic hose/pipe routing with several conflicting objectives. Within the thesis, methods have been developed and applied to single objective hose routing, multiobjective hose routing and multihose routing. The use of simulation and optimization in engineering design has been widely applied in all fields of engineering as the computational capabilities of computers has increased and improved. As a result of this, the application of nondeterministic optimization techniques such as genetic algorithms, simulated annealing algorithms, ant colony algorithms, etc. has increased dramatically resulting in vast improvements in the design process. Initially, two versions of ant colony algorithms have been developed based on, respectively, a random network and a grid network for a single objective (minimizing the length of the hoses) and avoiding obstacles in the CAD model. While applying ant colony algorithms for the simulation of hose routing, two
Ant Colony Based Approach for Solving FPGA routing
"... This paper is based on an ant colony optimization algorithm (ASDR) for solving FPGA routing for a route based routing constraint model in FPGA design architecture. I n this approach FPGA routing task is transformed into a Boolean Satisfiabilty (SAT) equation with the property that any assignment of ..."
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This paper is based on an ant colony optimization algorithm (ASDR) for solving FPGA routing for a route based routing constraint model in FPGA design architecture. I n this approach FPGA routing task is transformed into a Boolean Satisfiabilty (SAT) equation with the property that any assignment of input variables that satisfies the equation specifies a v alid route. The Satisfiability equation is then modeled as Constraint Satisfaction problem, which helps in reducing procedural programming. Satisfying assignment for particular route will result in a valid routing and absence of a satisfying assignment implies that the layout is unroutable. In second step ant colony optimization algorithm is applied on the Boolean equation for solving routing alternatives utilizing approach of hard combinatorial optimization problems. The experimental results suggest that the developed ant colony optimization algorithm based router has taken extremely short CPU time as compared to classical Satisfiabilty based detailed router (SDR) and finds all possible routes even for large FPGA circuits.
Multiobjective Combinatorial Optimization by Using Decomposition and Ant Colony
"... Combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), this paper proposes a multiobjective evolutionary algorithm, MOEA/DACO. Following other MOEA/Dlike algorithms, MOEA/DACO decomposes a multiobjective optimization problem into a numbe ..."
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Combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), this paper proposes a multiobjective evolutionary algorithm, MOEA/DACO. Following other MOEA/Dlike algorithms, MOEA/DACO decomposes a multiobjective optimization problem into a number of single objective optimization problems. Each ant (i.e. agent) is responsible for solving one subproblem. All the ants are divided into a few groups and each ant has several neighboring ants. An ant group maintains a pheromone matrix and an individual ant has a heuristic information matrix. During the search, each ant also records the best solution found so far for its subproblem. To construct a new solution, an ant combines information from its group’s pheromone matrix, its own heuristic information matrix and its current solution. An ant checks the new solutions constructed by itself and its neighbors, and updates its current solution if it has found a better one in terms of its own objective. Extensive experiments have been conducted in this paper to study and compare MOEA/DACO with other algorithms on two set of test problems. On the multiobjective 01 knapsack problem, MOEA/DACO outperforms MOEA/DGA on all the nine test instances. We also demonstrate that the heuristic information matrices in MOEA/DACO are crucial to the good performance of MOEA/DACO for the knapsack problem. On the biobjective traveling salesman problem, MOEA/DACO performs much better than BicriterionAnt on all the 12 test instances. We also evaluate the effects of grouping, neighborhood and the location information of current solutions on the performance of MOEA/DACO. The work in this paper shows that reactive search optimization scheme, i.e., the “learning while optimizing” principle, is effective in improving multiobjective optimization algorithms.
PopulationACO for the Automotive Deployment Problem Evolutionary Multiobjective Optimisation
"... The automotive deployment problem is a realworld constrained multiobjective assignment problem in which software components must be allocated to processing units distributed around a car’s chassis. Prior work has shown that evolutionary algorithms such as NSGAII can produce good quality solutio ..."
Abstract
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The automotive deployment problem is a realworld constrained multiobjective assignment problem in which software components must be allocated to processing units distributed around a car’s chassis. Prior work has shown that evolutionary algorithms such as NSGAII can produce good quality solutions to this problem. This paper presents a populationbased ant colony optimisation (PACO) approach that uses a single pheromone memory structure and a range of local search operators. The PACO and prior NSGAII are compared on two realistic problem instances. Results indicate that the PACO is generally competitive with NSGAII and performs more effectively as problem complexity—size and number of objectives—is increased.