Extremal optimization

About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.

Multi-Objective Extremal Optimization in Processor Load Balancing for Distributed Programs

Abstract: The paper presents a multi-objective load balancing algorithm based on Extremal Optimization in execution of distributed programs. The Extremal Optimization aims in defining task migration as a means for improving balance in loading executive processors with program tasks. In the proposed multi-objective approach three objectives relevant in processor load balancing for distributed applications are jointly optimized. These objectives include: balance in computational load of distributed processors, total volume of inter-processor communication between tasks and task migration metrics. In the proposed Extremal Optimization algorithms a special approach called Guided Search is applied in selection of a new partial solution to be improved. It is supported by some knowledge of the problem in terms of computational and communication loads influenced by task migration. The proposed algorithms are assessed by simulation experiments with distributed execution of program macro data flow graphs.

A computational optimization research on ant colony optimization for the traveling salesman problem

Abstract: The traveling salesman problem (TSP) is one of typical combinatorial optimization problems. Ant colony optimization (ACO) is an effective method to solve the traveling salesman problem, but there are some non-negligible shortcomings hidden in the original algorithm. The primary objective of this research is to optimize the ACO to produce quality work throughout solving TSP. To this end, the hybrid SOS-MMAS algorithm is proposed. Concretely, apply the advanced Max-Min Ant System (MMAS) as the basic algorithm to raise task scheduling efficiency, meanwhile introduce symbiotic organisms search (SOS) into the MMAS to optimize the key parameters. Experiments were carried out on typical TSP instances of different scales, and the SOS-ACO and ACO algorithms were compared with SOS-MMAS, which proved the excellent performance of SOS-MMAS in solving TSP. Rationality of the algorithm design and high performance has been illuminated by experimentation. In addition, the model also could serve to suggest further research of TSP or other related areas.

Research on Optimization of Traveling Salesman Problem Based on Differential Evolution Algorithm

Abstract: The traveling salesman problem is one of the classic problems of graph theory in the field of operations research. In real life, many practical application problems, such as delivery routes of express companies, can be modelled as traveling salesman problems through simplified processing. The differential evolution algorithm is a kind of optimization algorithm that has emerged not long ago. Based on the idea of standard differential evolution algorithm, we design an evolutionary algorithm for solving the traveling salesman problem. According to different mutation strategies, new mutation operators are mixed, and adaptive strategies are added to make the parameters dynamic, and the algorithm is applied to the two cases of TSPLIB. Finally, the results are compared with other mainstream basic algorithms. The algorithm results show that the algorithm we designed has better performance for solving small-scale problems, can solve large-scale traveling salesman problems, and has stronger optimization ability than other mainstream algorithms. However, it will fall into a local optimum, and the stability of the algorithm needs to be improved.