Golden Ball Algorithm for solving Flow Shop Scheduling Problem

The Flow Shop Scheduling Problem (FSSP) is notoriously NP-hard combinatorial optimization problem. The goal is to find a schedule that minimizes the makespan. This paper proposes an adaptation of a new approach called Golden Ball Algorithm (GBA). The proposed algorithm has been never tested with FSSP; it’s based on soccer concept to obtain the optimal solution. Numerical results are presented for 22 instances of OR- Library. The computational results indicate that this approach is practical for small OR-Library instances

A particle swarm optimization for the job shop scheduling problems

Popülasyon temelli sezgisel yöntemlerden biri olan Parçacık Sürü Optimizasyonu (PSO), kuş ve balık sürülerinin sosyal davranışlarından etkilenerek geliştirilen yeni bir eniyileme yöntemidir. Bu makalede, zor çizelgeleme problemleri arasında yer alan Atölye Tipi Çizelgeleme problemlerinin çözümü için, bir PSO modeli, Değişken Komşuluk Arama yöntemi ile birlikte geliştirilmiştir. Oluşturulan bu model, tamamlanma zamanı performans ölçütüne göre literatürde yer alan bazı zor test problemleri üzerindeki sonuçları incelenmiş ve iyi sonuçlar veren diğer sezgisel yöntemlerin sonuçlarıyla karşılaştırılmıştır. Sonuçta genel olarak önerilen modelin diğer yöntemlere göre daha iyi veya eşdeğer seviyede olduğu görülmüştür.  Anahtar Kelimeler: Atölye tipi çizelgeleme, parçacık sürü optimizasyonu, sezgiseller.Particle Swarm Optimization (PSO) is one of the population based optimization technique inspired by social behavior of bird flocking and fish schooling. PSO inventers were inspired of such natural process based scenarios to solve the optimization problems. In PSO, each single solution, called a particle, is considered as a bird, the group becomes a swarm (population) and the search space is the area to explore. Each particle has a fitness value calculated by a fitness function, and a velocity of flying towards the optimum, food. All particles fly across the problem space following the particle nearest to the optimum. PSO starts with initial population of solutions, which is updated iteration-by-iteration. Therefore, PSO can be counted as an evolutionary algorithm besides being a metaheuristics method, which allows exploiting the searching experience of a single particle as well as the best of the whole swarm. In this paper, A PSO model for the job shop scheduling problem is proposed. In addition, a simple but efficient local search method called Variable Neighborhood Search (VNS) is embedded to the PSO model and applied to several hardest benchmark suites. The results for the PSO algorithm with VNS are also presented and compared with many efficient meta-heuristic algorithms in literature. As a final result, PSO with VNS results are generally found to be better than other results. Keywords: Job shop scheduling, particle swarm optimization, Meta-Heuristics

Bees Algorithm: Theory, improvements and applications

In this thesis, a new population-based search algorithm called the Bees Algorithm (BA) is presented. The algorithm mimics the food foraging behaviour of swarms of honey bees. In its basic version, the algorithm performs a kind of neighbourhood search combined with random search and can be used for both combinatorial and functional optimisation. In the context of this thesis both domains are considered. Following a description of the algorithm, the thesis gives the results obtained for a number of complex problems demonstrating the efficiency and robustness of the new algorithm. Enhancements of the Bees Algorithm are also presented. Several additional features are considered to improve the efficiency of the algorithm. Dynamic recruitment, proportional shrinking and site abandonment strategies are presented. An additional feature is an evaluation of several different functions and of the performance of the algorithm compared with some other well-known algorithms, including genetic algorithms and simulated annealing. The Bees Algorithm can be applied to many complex optimisations problems including multi-layer perceptrons, neural networks training for statistical process control and the identification of wood defects in wood veneer sheets. Also, the algorithm can be used to design 2D electronic recursive filters, to show its potential in electronics applications. A new structure is proposed so that the algorithm can work in combinatorial domains. In addition, several applications are presented to show the robustness of the algorithm in various conditions. Also, some minor modifications are proposed for representations of the problems since it was originally developed for continuous domains. In the final part, a new algorithm is introduced as a successor to the original algorithm. A new neighbourhood structure called Gaussian patch is proposed to reduce the complexity of the algorithm as well as increasing its efficiency. The performance of the algorithm is tested by use on several multi-model complex optimisation problems and this is compared to the performance of some well-known algorithms

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field