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 ...

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...

Techniques that combine myopic problem specific methods and a meta-strategy, which guides the search out of local optimum, are currently providing the best results. Such hybrid methods are known as iterated local search algorithms or meta-heuristics. Despite the good results achieved by these methods the inability of many current meta-heuristics to provide a wide exploration of the search space...

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...

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...

Flexible flow shop (or a hybrid flow shop) scheduling problem is an extension of classical flow shop scheduling problem. In a simple flow shop configuration, a job having ‘g’ operations is performed on ‘g’ operation centres (stages) with each stage having only one machine. If any stage contains more than one machine for providing alternate processing facility, then the problem...

Meta-heuristic methods represent very powerful tools for dealing with hard combinatorial optimization problems. However, real life instances usually cannot be treated efficiently in "reasonable" computing times. Moreover, a major issue in metaheuristic design and calibration is to make them robust, i.e., to provide high performance solutions for a variety of problem settings. Parallel meta-heur...

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...