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We study nonconvex distributed optimization in multiagent networks where the communications between nodes is modeled as a time-varying sequence of arbitrary digraphs. We introduce a novel broadcast-based distributed algorithmic framework for the (constrained) minimization of the sum of a smooth (possibly nonconvex and nonseparable) function, i.e., the agents' sum-utility, plus a convex (possibly nonsmooth...
Recently, Multi-Objective Differential Evolution (MODE), powerful and efficient population-based stochastic processing, has become an indispensable algorithm for solving numerical optimization problems widely. It is found in various benchmark functions that traditional MODE is unable to search global optima completely, falling into local optima because only using one strategy to search global optimal...
This paper presents a first attempt towards the value-convergence time complexity analysis of colony optimization (ACO) on the first-order deceptive systems taking the n-bit trap problem as the test instance under consideration. We prove that time complexity of MMAS, which is an ACO with limitations of the pheromone on each edge, on n-bit trap problem is O(n2m.log n), here n is the size of the problem...
With the rapid development of electronic commerce and logistics distribution, multi-depot vehicle routing problem with backhauls (MDVRPB) as influencing electronic commerce more step development, has been paid more attentions. According to the characteristics of model, hybrid heuristic algorithm is used to get the optimization solution. First of all, use hybrid coding so as to simplify the problem;...
An adaptive parallel ant colony algorithm (PACO) is presented. In the algorithm, we propose a strategy for information exchange between processors which make each processor choose its partner to communicate and update the pheromone adaptively. We also propose a method of adjusting the time interval of information exchange adaptively according to the diversity of the solutions so as to increase the...
As more and more real-world optimization problems become increasingly complex, algorithms with more capable optimizations are also increasing in demand. For solving large scale global optimization problems, this paper presents a variation on the traditional PSO algorithm, called the efficient population utilization strategy for particle swarm optimizer (EPUS-PSO). This is achieved by using variable...
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