Meta-heuristics are methods that sit on top of local search algorithms. They perform the function of avoiding or escaping a local optimum and/or premature convergence. The aim of this paper is to survey, compare and contrast meta-heuristics for local search. First, we present the technique of local search (or hill climbing as it is sometimes known). We then present a table displaying the attrib...

this study considers scheduling in hybrid flow shop environment with unrelated parallel machines for minimizing mean of job's tardiness and mean of job's completion times. this problem does not study in the literature, so far. flexible flow shop environment is applicable in various industries such as wire and spring manufacturing, electronic industries and production lines. after mode...

We consider the problem of scheduling a number of jobs on a number of unrelated parallel machines in order to minimize the makespan. We develop three heuristic approaches, i.e., a genetic algorithm, a tabu search algorithm and a hybridization of these heuristics with a truncated branch-and-bound procedure. This hybridization is made in order to accelerate the search process to near-optimal solu...

In this paper we consider n-jobs, m-machines permutation flow shop scheduling problems. Flow shop scheduling is one of the most important combinational optimization problems. The permutation flow shop scheduling problems are NP-Hard (Non deterministic Polynomial time Hard). Hence many heuristics and metaheuristics were addressed in the literature to solve these problems. In this paper a hybrid ...

Meta-heuristics optimization methods are important techniques for optimal design of the engineering systems. Numerous methods, inspired by different nature phenomena, have been introduced in the literature. A new modified version of Teaching-Learning-Based Optimization (TLBO) Algorithm is introduced in this paper. TLBO, as a parameter free algorithm, is based on the learning procedure of studen...

Combinatorial optimization problems are often used to test heuris-tics. Among heuristics, stochastic ones deserve particular consideration being generally meta-heuristics that aim at performing reasonnably well on a wide spectrum of problems. Among them, evolutionary algorithms have recently appeared. Emphasis have been put on them by researches that have shown that they are able to solve eecie...

In this paper we present a Genetic Algorithm (GA) with problem-specific operators for the layered digraph drawing problem and compare it with two very different meta-heuristics: Tabu search and Multi-start descents. Tabu search has previously proved to be better than the classical deterministic local heuristic often employed in graph drawing. Here, we show on a set of 282 medium-sized graphs th...

A meta-heuristic approach for solving the flexible job-shop scheduling problem (FJSP) is presented in this study. This problem consists of two sub-problems, the routing problem and the sequencing problem and is among the hardest combinatorial optimization problems. We propose a Evolutionary Algorithm (EA) for the FJSP. Our algorithm uses several different rules for generating the initial popula...

The problem of m-machine permutation flowshop scheduling is considered in this paper. The objective is to minimize the makespan. The flowshop scheduling problem is a typical combinatorial optimization problem and has been proved to be strongly NP-hard. Hence, several heuristics and meta-heuristics were addressed by the researchers. In this paper, a discrete African wild dog algorithm is applied...