Extremal optimization

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

Proximate optimality principle based Tabu Search

Abstract: This paper proposes an algorithm-Multi Criteria Tabu Search coordinating the intensification and the diversification based on Proximate Optimality Principle (POP)-which has several advantages for solving combinatorial optimization problems. The proposed algorithm is applied to some traveling salesman problems which are typical combinatorial optimization problems in order to verify the performance of the proposed algorithm. The simulation results indicate that the proposed method has higher optimality than the conventional Tabu Search.

Solving three-objective optimization problems using evolutionary dynamic weighted aggregation: results and analysis

Abstract: The main purposes of this paper is twofold. First, the evolutionary dynamic weighted aggregation (EDWA) [1] approaches are extended to the optimization of three-objective problems. Fig. 1 shows two example patterns for weight change. Through two three-objective test problems [2], the methods have shown to be effective. Theoretical analyses reveal that the success of the weighted aggregation based methods can largely be attributed to the following facts:

A perspective view on Travelling Salesman Problem using genetic algorithm

Abstract: The Travelling Salesman Problem (TSP) is one of the extensively studied optimization problem. The numerous direct applications of the TSP bring life to the research area and help to direct future work. To solve this problem many techniques have been developed. Genetic algorithm is one among those which solves this problem by using the processes observed in natural evolution to solve various optimizations and search problems. This paper presents a complete survey on genetic algorithm techniques proposed by researchers for solving Travelling Salesman Problem.

Ant colony optimization algorithm (ACO): a new heuristic approach for engineering optimization

Abstract: Over the last decade, evolutionary and meta-heuristic algorithms have been extensively used as search and optimization tools in various problem domains, including science, commerce, and engineering. Their broad applicability, ease of use, and global perspective may be considered as the primary reason for their success. Ant colony foraging behavior may also be considered as a typical swarm-based approach to optimization. In this paper, ant colony optimization algorithm (ACO) is presented and tested with few benchmark examples. To test the performance of the algorithm, three benchmarks constrained and/or unconstrained real valued mathematical models were selected. The first example is the Ackley's function which is a continuous and multimodal test function obtained by modulating an exponential function with a cosine wave of moderate amplitude. The algorithm application resulted in the global optimal with reasonable CPU time. To show the efficiency of the algorithm in constraint handling, the model was applied to a two-variable, two constraint highly nonlinear problem. It was shown that the performance of the model is quite comparable with the results of well developed GA. The third example is a real world water resources operation optimization problem. The developed model was applied to a single reservoir with 60 periods with objective of minimizing the total square deviation from target demand. Results obtained are quit promising and compares well with the results of some other well-known heuristic approaches.