this study emphasized the ability of genetic algorithm and cellular automate to simulate urban land use changes by integrating adaptive model. the most important part of modeling is to define transition rules.  in this research, a cellular automata model in dinamica ego software was used coupled with genetic algorithm. according to disability of the software for manipulating large number of var...

The reliability-based heuristic search methods for maximum likelihood decoding (MLD) generate test error patterns (or, equivalently, candidate codewords) according to their heuristic values. Test error patterns are stored in lists and its space complexity is crucially large for MLD of long block codes. One of the well-known heuristic search methods for MLD is the A decoding algorithm proposed b...

Meta-heuristic algorithms are applied in optimization problems in a variety of fields, including engineering, economics, and computer science. In this paper, seven population-based meta-heuristic algorithms are employed for size and geometry optimization of truss structures. These algorithms consist of the Artificial Bee Colony algorithm, Cyclical Parthenogenesis Algorithm, Cuckoo Search algori...

This paper presents the use of a cooperative co-evolutionary genetic algorithm (CCGA) in conjunction with a heuristic rule for solving a 3D container loading or bin packing problem. Unlike previous works which concentrate on using either a heuristic rule or an optimisation technique to find an optimal sequence of packages which must be loaded into the containers, the proposed heuristic rule is ...

We describe a hybrid meta-heuristic algorithm for combinatorial optimization problems with a specific reference to the travelling salesman problem (TSP). The method is a combination of a genetic algorithm (GA) and greedy randomized adaptive search procedure (GRASP). A new adaptive fuzzy a greedy search operator is developed for this hybrid method. Computational experiments using a wide range of...

Cloud computing is being welcomed as a new basis to manage and provide services on the internet. One of the reasons for increased efficiency of this environment is the appropriate structure of the tasks scheduler. Since the tasks scheduling in the cloud computing environment and distributed systems is an NP-hard problem, in most cases to optimize the scheduling issues, the meta-heuristic method...

flow shop sequencing problem is included in np-hard problems and a number of heuristic methods have been developed to solve it. most of these methods are offered for the crisp processing time. however fuzzy algorithm is appropriate for the problems with fuzzy processing time. this paper presents a method in which an algorithm is used to minimize make span of flow shop with fuzzy processing time...

This paper deals with a multi-period fixed charge production-distribution problem associated with backorder and inventories. The objective is to determine the size of the shipments from each supplier and backorder and inventories at each period, so that the total cost incurred during the entire period towards production, transportation, backorder and inventories is minimised. A 0-1 mixed intege...

in this paper, we consider a flow shop scheduling problem with availability constraints (fsspac) for the objective of minimizing the makespan. in such a problem, machines are not continuously available for processing jobs due to preventive maintenance activities. we proposed a mixed-integer linear programming (milp) model for this problem which can generate non-permutation schedules. furthermor...