Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Multi-objective pump scheduling optimisation using evolutionary strategies
Advances in Engineering Software - Special issue on evolutionary optimization of engineering problems
Journal of Systems and Software
Distributed simulation of agent-based systems with HLA
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Regular Paper: Interactive N-Body Simulations On the Grid: HLA Versus MPI
International Journal of High Performance Computing Applications
A particle swarm optimization algorithm for the multiple-level warehouse layout design problem
Computers and Industrial Engineering
A Pareto archive particle swarm optimization for multi-objective job shop scheduling
Computers and Industrial Engineering
Agent-Based Co-Operative Co-Evolutionary Algorithm for Multi-Objective Optimization
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
MOGAMOD: Multi-objective genetic algorithm for motif discovery
Expert Systems with Applications: An International Journal
DS-RT '08 Proceedings of the 2008 12th IEEE/ACM International Symposium on Distributed Simulation and Real-Time Applications
Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks
Environmental Modelling & Software
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
A genetic algorithm approach for multi-objective optimization of supply chain networks
Computers and Industrial Engineering
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
Game-theoretic validation and analysis of air combat simulation models
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
Knowledge and Information Systems
Journal of Parallel and Distributed Computing
Partner selection in a virtual enterprise under uncertain information about candidates
Expert Systems with Applications: An International Journal
IEEE Transactions on Computers
Energy Conscious Scheduling for Distributed Computing Systems under Different Operating Conditions
IEEE Transactions on Parallel and Distributed Systems
International Journal of Computer Integrated Manufacturing
IEEE Transactions on Services Computing
A probabilistic task scheduling method for grid environments
Future Generation Computer Systems
Evaluation of gang scheduling performance and cost in a cloud computing system
The Journal of Supercomputing
Synthesizing a predatory search strategy for VLSI layouts
IEEE Transactions on Evolutionary Computation
Parameter optimization of an on-chip voltage reference circuitusing evolutionary programming
IEEE Transactions on Evolutionary Computation
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A Hybrid Quantum-Inspired Genetic Algorithm for Multiobjective Flow Shop Scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Multi-objective combinatorial optimization (MOCO) is an essential concern for the implementation of large-scale distributed modeling and simulation (MS) system. It is more complex than general computing systems, with higher dynamics and stricter demands on real-time performance. The quality and speed of the optimal decision directly decides the efficiency of the simulation. However, few works have been carried out for multi-objective combinatorial optimization MOCO especially in large-scale and service-oriented distributed simulation systems (SoDSSs). The existing algorithms for MOCO in SoDSSs are far from enough owing to their low accuracy or long decision time. To overcome this bottleneck, in this paper, a quantum multi-agent evolutionary algorithm (QMAEA), for addressing MOCO in large-scale SoDSSs is proposed. In QMAEA, the concept and characteristics of agent and quantum encoding are introduced for high intelligent searching. Each agent represented by a quantum bit, called a quantum agent (QAgent), is defined as a candidate solution for a MOCO problem, and each QAgent is assigned an energy, which denotes the fitness or objective function value of the candidate solution represented by it. Each QAgent is connected by four other QAgents nearby, and all QAgents are organized by an annular grid, called a multi-agent grid (MAG). In a MAG system, the population of QAgents can reproduce, perish, compete for survival, observe and communicate with the environment, and make all their decisions autonomously. Several operators, i.e. energy-evaluation-operator, competition-operator, crossover-operator, mutation-operator and trimming-operator, are designed to specify the evolvement of the MAG. The theory of predatory search strategy of animals is introduced in the evolution of QMAEA. Multiple evolutionary strategies, such as local-evolution-strategy, local-mutation-strategy and global-mutation-strategy are designed and used to balance the exploration (global search ability) and the exploitation (local search ability) of QMAEA. The framework and procedures of QMAEA are presented in detail. The simulation and comparison results demonstrate the proposed method is very effective and efficient for addressing MOCO in SoDSSs.