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 ...

Meta-heuristics are high-level approaches developed to discover a heuristic that provides reasonable solution many varieties of optimization problems. The classification problems contain sort problem. Simply, the objective herein is reduce number misclassified instances. In this paper, question whether meta-heuristic methods can be used construct linear models or not answered. To end, Particle ...

The core of artificial intelligence and machine learning is to get computers to solve problems automatically. One of the great tools that attempt to achieve that goal is Genetic Programming (GP). GP is a generalization procedure of the well-known meta-heuristic of Genetic Algorithms (GAs). Meta-heuristics have shown successful performance in solving many combinatorial search problems. In this p...

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...

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...

The resource-constrained project scheduling problem (RCPSP) is a well-known that has attracted attention since several decades. Despite the rapid progress of exact and (meta-)heuristic procedures, can still not be solved to optimality for many instances relatively small size. Due known complexity, researchers have proposed fast efficient meta-heuristic solution procedures solve near optimality....

in some industries as foundries, it is not technically feasible to interrupt a processor between jobs. this restriction gives rise to a scheduling problem called no-idle scheduling. this paper deals with scheduling of no-idle open shops to minimize maximum completion time of jobs, called makespan. the problem is first mathematically formulated by three different mixed integer linear programming...

in this study, we discuss the capacitated facility location-allocation problem with uncertain parameters in which the uncertainty is characterized by given finite numbers of scenarios. in this model, the objective function minimizes the total expected costs of transportation and opening facilities subject to the robustness constraint. to tackle the problem efficiently and effectively, an effici...

In finance, the most efficient portfolio is tangency portfolio, which formed by intersection point of frontier and capital market line. This paper defines explores a time-varying under nonlinear constraints (TV-TPNC) problem as programming (NLP) problem. Because meta-heuristics are commonly used to solve NLP problems, semi-integer beetle antennae search (SIBAS) algorithm proposed for solving ca...

We propose a new population-based hybrid meta-heuristic for the periodic vehicle routing problem with time windows. Two neighborhood-based meta-heuristics are used to educate the offspring generated by a new crossover operator to enhance the solution quality. This hybridization provides the means to combine the exploration capabilities of population-based methods and the systematic, sometimes a...