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Based on the problem of the multi-objective spectrum allocation on the joint optimization of maximal network utility and the fairness of users in the cognitive network, a novel adaptive fruit fly algorithm (FOA) for channel (i.e. spectrum) scheduling is proposed, which can make both of primary users (PUs) and cognitive nodes (CNs) access to the available wireless channels. In the present algorithm,...
In this paper, a new type of TSP, i.e. PTSP (Polymorphic Traveling Salesman Problem) is proposed. The problem is discovered from the research of scan field route optimization. As for PTSP, every node is polymorphic, which means each node can have several states, but obtains only a determined state in a determined loop, moreover a path which connects a pair of nodes can be different and have different...
The multidimensional assignment problem (MAP) is a natural extension of the well known assignment problem. A problem with s dimensions is called a SAP. The most studied NP-hard case of the MAP is the 3AP. Memetic algorithms have been proven to be the most effective technique to solve MAP. The use of powerful local search heuristics in combination with a genetic algorithm, even if it has a simple structure,...
Existing clustering techniques primarily rely on prior knowledge about the data, such as the number of clusters and radii. However, in real applications, the number of clusters and the radii of clusters are usually unknown. Therefore, the performance of clustering methods with overlapping data is degraded due to their limitations in finding all cluster centers with uneven density values. Hence, a...
A Cooperated fruit fly optimization algorithm (CFOA) is proposed for knapsack problems. In CFOA, a group generating strategy is designed for generating the initial solution. A novel cooperation strategy is used to enhance the connection and communication between flies. A repair operator based on value-weight ratio of each item is employed to guarantee the feasibility of the solution and enhance the...
For task completion in distributed environments, a set of resources is required and a group of agents must cooperate in deciding the share each should provide to maximize the system performance. We address the problem from an evolutionary game-theoretic perspective and present a fully distributed algorithm based on local replicator dynamics. By using the optimality condition, we prove the convergence...
Constrained dynamic matrix control often needs to solve a nonlinear optimization problem with constraints. The traditional solution method is highly dependent on the prediction model and is easy to fall into the local minimum. To solve this problem, an improved differential evolution algorithm is applied to solve the optimal control law, and the optimization problem with constraints is dealt with...
In this study, Vehicle Routing Problem with Time Windows (VRPTW) with known customer demands, a single depot and identical vehicles, is considered. Minimizing the total distance and the total waiting time of the vehicles are determined as objective functions for VRPTW which is capable to serve the customers in a prespecified time interval. A hybridized version of genetic algorithm which is a metaheuristic...
In this paper, an adaptive genetic algorithm based on multi-population elite selection strategy is proposed. The multi-population elite selection strategy is used to preserve the optimal individuals of each group. Finally, these optimal individuals formed a population, and then use the improved adaptive genetic algorithm to finish the solution. By comparing the simulation experiments of TSP problem...
Recently, scalarizing-function based approaches have received a renewed interest in multi-objective optimization. In fact, the simplicity of these functions allows them to be easily adaptable to any resolution method. In this contribution, we propose an iterated multi-objective local search algorithm, called MoLSAugWT, based on well-known and frequently-used scalarizing-function: the augmented weighted...
This paper presents algorithm for optimal reconfiguration of distribution networks using hybrid heuristic genetic algorithm. Improvements introduced in this approach make it suitable for real-life networks with realistic degree of complexity and network size. The algorithm introduces several improvements related to the generation of initial set of possible solutions as well as crossover and mutation...
This paper presents a novel multi-objective Spiral Dynamic Optimization (MOSDA) algorithm. It is an extended version of a single objective type SDA. A Non-dominated sorting (NS) approach from Non-dominated Sorting Genetic Algorithm II (NSGAII) is adopted into SDA to develop its multi-objective (MO) type algorithm. SDA has a good elitism strategy and a simple structure. On the other hand, NS is a fast...
Task scheduling is critical for obtaining a high performance schedule in heterogeneous computing systems (HCS) and searching an optimal scheduling solution has been shown to be NP-complete. In this paper, a hybrid heuristicgenetic algorithm with adaptive parameter (HGAAP) is proposed by combining a heuristic scheduling algorithm and a genetic algorithm. An existing common heuristic scheduling algorithm...
The particle swarm optimization algorithm is improved by introducing the immune selection, adaptive propagation, multi-population evolution. An improved adaptive propagation chaotic particle swarm optimization algorithm based on immune selection (IS-APCPSO algorithm for short) is proposed in this paper. The performance of several algorithms has been compared by a classic example of traffic network...
To improve the measuring accuracy of planar curve profile error, an improved genetic algorithm is put forward to realize self-adaptive matching of measured curve, eliminating the position deviation during error evaluation of planar curve profile. It not only improves the efficiency and precision of the algorithm but also prevents premature convergence to local optimal solutions by introducing a relative...
Evolutionary algorithm hybridizing with A-means operation has been widely employed for data clustering. The A-means operation in this approach, however, is generally applied with a fixed number of iteration and at each generation (i.e., fixed intensity and frequency) during evolution, which could be far more than optimal. In this paper, we first introduce a generalized A-means usage framework, which...
Preserving population diversity is crucial for the performance of multi-objective evolutionary algorithms (MOEAs). In this paper, we propose a novel dynamic crowding distance based diversity preserving strategy for MOEAs. In the proposed strategy, the crowding distance is calculated based on the degree of deviation of each individual to its adjacent neighbors, thus appropriately adjusting the individual's...
Based on the quantum genetic algorithm, a new improved quantum genetic algorithm is proposed by introducing the grouping optimization strategy of hybrid shuffled frog-leaping algorithm and combining the quantum variation and simulated annealing receiving criteria. The improved algorithm is applied to function optimization. The experimental results show that the proposed algorithm can improve the performance...
Production scheduling is the key one of the basic means of production management, and the production scheduling optimization is one of the core technologies of modern management technology. According to the characteristics of quick response and order driven of production in textile machinery manufacturing enterprise, an optimal production scheduling mode is proposed which is based on improved bee...
Most of the existing population behavior studies are about the analysis of the population dynamic behavior of genetic algorithm, while there is little analysis of the population dynamic behavior of particle swarm optimization (PSO). Therefore, there is an urgent need for a new method to characterize the population dynamic behavior of PSO in the search process. In this paper, we propose some metrics...
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