In this research application paper, the usefulness of an intelligent mechanism (a cubic spline smoothing technique) for determining when to switch from one algorithm to another within a meta heuristic search process is explored. We concentrated on a typical planning problem for a southern United States forestry company where the net present value of management activities is maximized subject to...

In this paper, we show a survey of meta-heuristics algorithms based on grouping of animals by social behavior for the Traveling Salesman Problem, and propose a new classification of meta-heuristics algorithms (not based on swarm intelligence theory) based on grouping of animals: swarm algorithms, schools algorithms, flocks algorithms and herds algorithms: a) The swarm algorithms (inspired by th...

In this paper, we revisit the idea of splitting a planning problem into subproblems hopefully easier to solve with the help of landmark analysis. This technique initially proposed in the first approaches related to landmarks in classical planning has been outperformed by landmark-based heuristics and has not been paid much attention over the last years. We believe that it is still a promising r...

We consider the problem of sequential portfolio selection in the stock market. There are theoretically well grounded algorithms for the problem, such as Universal Portfolio (UP), Exponentiated Gradient (EG) and Online Newton Step (ONS). Such algorithms enjoy the property of being universal, i.e., having low regret with the best constant rebalanced portfolio. However, the practical performance o...

In recent years, there has been a growing research interest in integrating machine learning techniques into meta-heuristics for solving combinatorial optimization problems. This integration aims to lead toward an efficient, effective, and robust search improve their performance terms of solution quality, convergence rate, robustness. Since various methods with different purposes have developed,...

This study focusses on a two objective type-2 simple assembly line balancing problem. Its primary is minimizing the cycle time, or equivalently, maximizing production rate of line. Minimization workload imbalance among workstations considered as secondary objective. Since problem known to be intractable, reactive tabu search algorithm proposed for solution. Although well-known meta-heuristic pr...

In single-objective optimization it is possible to find a global optimum, while in the multi-objective case no optimal solution is clearly defined, but several that simultaneously optimize all the objectives. However, the majority of this kind of problems cannot be solved exactly as they have very large and highly complex search spaces. Recently, meta-heuristic approaches have become important ...

[1] Corresponding author e-mail: [email protected]   [1] Corresponding author e-mail: [email protected]   Lot-sizing problems (LSPs) belong to the class of production planning problems in which the availability quantities of the production plan are always considered as a decision variable. This paper aims at developing a new mathematical model for the multi-level ca...

Population-based meta-heuristics are algorithms that can obtain very good results for complex continuous optimization problems in a reduced amount of time. These search algorithms use a population of solutions to maintain an acceptable diversity level during the process, thus their correct distribution is crucial for the search. This paper introduces a new population meta-heuristic called ‘‘var...

Background Many combinatorial optimization problems are NP-hard, and the theory of NP-completeness has reduced hopes that NP-hard problems can be solved within polynomially bounded computation times (Dahlke 2008; Dunne 2008). Nevertheless, sub-optimal solutions are sometimes easy to find. Consequently, there is much interest in approximation and heuristic algorithms that can find near optimal s...