Heuristic and Cost-Based Optimization for Diverse Provenance Tasks

A Framework for Adapting Population-Based and Heuristic Algorithms for Dynamic Optimization Problems

In this paper, a general framework was presented to boost heuristic optimization algorithms based on swarm intelligence from static to dynamic environments. Regarding the problems of dynamic optimization as opposed to static environments, evaluation function or constraints change in the time and hence place of optimization. The subject matter of the framework is based on the variability of the ...

Chaotic-based Particle Swarm Optimization with Inertia Weight for Optimization Tasks

Among variety of meta-heuristic population-based search algorithms, particle swarm optimization (PSO) with adaptive inertia weight (AIW) has been considered as a versatile optimization tool, which incorporates the experience of the whole swarm into the movement of particles. Although the exploitation ability of this algorithm is great, it cannot comprehensively explore the search space and may ...

Clustering and Memory-based Parent-Child Swarm Meta-heuristic Algorithm for Dynamic Optimization

So far, various optimization methods have been proposed, and swarm intelligence algorithms have gathered a lot of attention by academia. However, most of the recent optimization problems in the real world have a dynamic nature. Thus, an optimization algorithm is required to solve the problems in dynamic environments well. In this paper, a novel collective optimization algorithm, namely the Clus...

Learning heuristic functions for cost-based planning

In the last International Planning Competition (IPC 2011), the most efficient planners in the satisficing track were planners that used unit-cost heuristics. These heuristics ignore the real cost of the actions and return instead an estimate of the plan length to the goal. The main advantage of these heuristics compared with real-cost heuristics is that they solve a greater number of problems (...

Diverse Classifiers for NLP Disambiguation Tasks Comparison, Optimization, Combination, and Evolution