Fehmi Burcin Ozsoydan

An improved firefly algorithm for solving dynamic multidimensional knapsack problems

There is a wide range of publications reported in the literature, considering optimization problems where the entire problem related data remains stationary throughout optimization. However, most of the real-life problems have indeed a dynamic nature arising from the uncertainty of future events. Optimization in dynamic environments is a relatively new and hot research area and has attracted notable attention of the researchers in the past decade. Firefly Algorithm (FA), Genetic Algorithm (GA) and Differential Evolution (DE) have been widely used for static optimization problems, but the applications of those algorithms in dynamic environments are relatively lacking. In the present study, an effective FA introducing diversity with partial random restarts and with an adaptive move procedure is developed and proposed for solving dynamic multidimensional knapsack problems. To the best of our knowledge this paper constitutes the first study on the performance of FA on a dynamic combinatorial problem. In order to evaluate the performance of the proposed algorithm the same problem is also modeled and solved by GA, DE and original FA. Based on the computational results and convergence capabilities we concluded that improved FA is a very powerful algorithm for solving the multidimensional knapsack problems for both static and dynamic environments.

Adaptive firefly algorithm with chaos for mechanical design optimization problems

Proposing an extension of firefly algorithm.Employment of picewise chaos, for an further enhanced diversity.Making use of a simple but effective constraint handling method.Making use of an improved local search procedure. Firefly algorithm (FA) is a newer member of bio-inspired meta-heuristics, which was originally proposed to find solutions to continuous optimization problems. Popularity of FA has increased recently due to its effectiveness in handling various optimization problems. To enhance the performance of the FA even further, an adaptive FA is proposed in this paper to solve mechanical design optimization problems, and the adaptivity is focused on the search mechanism and adaptive parameter settings. Moreover, chaotic maps are also embedded into AFA for performance improvement. It is shown through experimental tests that some of the best known results are improved by the proposed algorithm.

A multi-population firefly algorithm for dynamic optimization problems

In traditional optimization problems, problem domain, constraints and problem related data are assumed to remain stationary throughout the optimization process. However, numerous real life optimization problems are indeed dynamic in their nature due to unpredictable events such as due date changes, arrival of new jobs or cancellations. In the literature, a problem with one of these features is referred as dynamic optimization problem (DOP). In contrast to static optimization problems, in DOPs, the aim is not only to find the optimum of the current configuration of a problem environment, but to track and find the changing optima. The field of dynamic optimization is a hot research area and it has attracted a remarkable attention of researchers. A considerable number of recent studies on DOPs usually employs bio-inspired metaheuristic algorithms, which are efficient on a wide range of static optimization problems. In the present work, a multi-population firefly algorithm with chaotic maps is proposed to solve DOPs. The tests are conducted on the well known moving peaks benchmark problem. In regard to the results, the proposed algorithm is found as a promising approach for the present problem.

Heuristic solution approaches for the cumulative capacitated vehicle routing problem

Cumulative capacitated vehicle routing problem (CCVRP) is an extension of the well-known capacitated vehicle routing problem, where the objective is minimization of sum of the arrival times at nodes instead of minimizing the total tour cost. This type of routing problem arises when a priority is given to customer needs or dispatching vital goods supply after a natural disaster. This paper focuses on comparing the performances of neighbourhood and population-based approaches for the new problem CCVRP. Genetic algorithm (GA), an evolutionary algorithm using particle swarm optimization mechanism with GA operators, and tabu search (TS) are compared in terms of required CPU time and obtained objective values. In addition, a nearest neighbourhood-based initial solution technique is also proposed within the paper. To the best of authors’ knowledge, this paper constitutes a base for comparisons along with GA, and TS for further possible publications on the new problem CCVRP.

An improved approach for determination of index positions on CNC magazines with cutting tool duplications by integrating shortest path algorithm

Optimisation of automatic tool changer (ATC) indexing problem, where cutting tools are allocated to the stations on a turret magazine of a CNC machine, is one of the challenging problems in machining. The aim of the problem is to minimise the total indexing time of ATC. This problem becomes even more challenging if duplication of cutting tools is allowed and a bidirectional ATC is used. The problem has a unique feature which has not been stressed yet by other researchers, that is, although ATC indexing (master problem) is the main optimisation problem, objective function evaluation of this problem is a standalone optimisation problem (sub problem) indeed. Although an approximation algorithm does not guarantee optimality for the master problem, the subproblem must be solved optimally; otherwise, deficiencies arising from ill-defined objective function might be encountered. Considering this interesting future, a novel methodology, which employs a shortest path algorithm, is developed. Thus, the subproblem of this complicated problem can be optimally solved. Moreover, two metaheuristics, based on threshold accepting and descent first improvement greedy methodologies, are proposed for generating efficient solutions. Finally, several benchmarking instances are generated and solved to test the proposed algorithms.

Evolutionary and population-based methods versus constructive search strategies in dynamic combinatorial optimization

Abstract Optimization in dynamic environments is a hot research area that has attracted a notable attention in the past decade. It is clear from the dynamic optimization literature that most of the effort is devoted to continuous dynamic optimization problems although majority of the real-life problems are combinatorial. Additionally, in comparison to evolutionary or population-based approaches, constructive search strategy, which is shown to be successful in stationary combinatorial optimization problems, is commonly ignored by the dynamic optimization community. In the present work, a constructive and multi-start search strategy is proposed to solve dynamic multi-dimensional knapsack problem, which has numerous applications in real world. Making use of constructive and multi-start features, the aim here is to test the performance of such a strategy and to observe its behavior in dynamically changing environments. In this regard, this strategy is compared to the well-known evolutionary and population-based approaches, including a Genetic Algorithm-based memetic algorithm, Differential Evolution algorithm, Firefly Algorithm and a hyper-heuristic, which employs these population-based algorithms as low-level heuristics in accordance with their individual contributions. Furthermore, in order to improve their performances in dynamic environments, the mentioned evolutionary algorithms are enhanced by using triggered random immigrants and adaptive hill climbing strategies. As one can see from the comprehensive experimental analysis, while the proposed approach outperforms most of the evolutionary-based approaches, it is outperformed by firefly and hyper-heuristic algorithms in some of the instances. This points out competiveness of the proposed approaches. Finally, according to the statistical results of non-parametric tests, one can conclude that the proposed approach can be considered as a promising and a competitive algorithm in dynamic environments.

Dynamic optimization in binary search spaces via weighted superposition attraction algorithm

Abstract Optimization in dynamic environments is a fast developing research area. Several outstanding metaheuristic algorithms were proposed to solve dynamic optimization problems (DOPs) in the past decade. However, most of the effort is devoted to real-valued DOPs. Although, great majority of real-life problems has discrete and binary spaces, research in binary DOPs is still lacking. Accordingly, the present study introduces the first binary DOP application of Weighted Superposition Attraction Algorithm (WSA), which is a new generation swarm intelligence-based metaheuristic algorithm. As a distinctive feature from the existing literature, the introduced binary version of WSA (bWSA) does not require transfer functions for converting floating numbers to binary, whereas they are commonly employed in binary modifications of various metaheuristic algorithms. Additionally, some new extensions of bWSA are also developed in the present study. For comparative analysis, first, some state-of-the-art algorithms including Particle Swarm Optimization and Genetic Algorithm are adopted. As secondarily, another new-generation hot optimizer, namely, Firefly Algorithm (FA), which has already been shown to be quite promising in DOPs, is employed in the present work. Moreover, all algorithms implemented here are enhanced by using dualism-based search, triggered random immigrants and adaptive hill climbing strategies. Dynamic modifications of the well-known binary benchmarking problems such as One-Max, Plateau, Royal Road and Deceptive Functions are used in the computational study. Performances of the proposed algorithms are compared in detail. Finally, non-parametric statistical tests are employed to validate the results. Findings point out superiority of bWSA in binary DOPs.

A constructive search algorithm for combinatorial dynamic optimization problems

In most of the optimization studies, the problem related data is assumed to be exactly known beforehand and remain stationary throughout whole optimization process. However, majority of real life problems and their practical applications are dynamic in their nature due to the reasons arising from unpredictable events, such as rush orders, fluctuating capacities of manufacturing constraints, changes in costs or profits. A problem, carrying one of these features is known as dynamic optimization problem (DOP) in the related literature. In DOPs the aim is not only to find the optimum of the current problem configuration, but to keep track of the moving optima. Dynamic optimization is a hot research area and a notable variety of solution methodologies are developed for DOPs in the past decade. As a contribution to the existing literature of DOPs, in the current work, the idea of using a multi-start and constructive search algorithm and thus breaking the dependency to the previously found solutions is presented. The performance tests are conducted on the generalized assignment problem, which has numerous real life applications. In regard to the obtained results, the proposed method is found promising.

Minimisation of non-machining times in operating automatic tool changers of machine tools under dynamic operating conditions

In many optimisation studies, it is assumed that problem related data does not change once the generated solution plan or schedule is currently in use. However, majority of real-life manufacturing problems are time-varying in their nature due to unpredictable events such as changes in lot sizes, fluctuating capacities of manufacturing constraints, changes in costs or profits. A problem, which contains at least one of these feature is referred as dynamic optimisation problem (DOP) in the related literature. The present study introduces a practical industrial application of a DOP, emerging particularly in flexible manufacturing systems (FMSs), where numerically controlled machine tools with automatic tool changers are employed. It is already known in FMSs that minimisation of non-machining times is vital for an efficient use of scarce resources. Therefore, fast response to possible changes in production is crucial in order to attain flexibility. In this context, first, a benchmarking environment is created by making use of already published problems and by introducing dynamic events. Next, effective strategies, including simulated annealing (SA) algorithm along with SA with multiple starts are developed for the introduced problem. Numerical results show that the developed SA with multiple starts is a promising approach for the introduced problem.

Quantum firefly swarms for multimodal dynamic optimization problems

Abstract Optimization problems have attracted attention of researchers for decades. Commonly, problem related data and problem domain are assumed to be exactly known beforehand and to remain stationary. However, numerous real life optimization problems are dynamic. In practice, unpredictable events like due date changes, arrivals of new jobs or cancellations yield to changes in parameters, constraints or variables. In addition to the challenges of traditional stationary optimization problems, diverse parts of the problem space should also be monitored to keep track of moving optima in dynamic problems. Therefore, dividing a population (swarm) into smaller sized sub-swarms is a promising strategy particularly for multi-modal problems. In this context, the present work extends Firefly Algorithm (FA) as a multi-population based algorithm to solve multi-modal dynamic optimization problems due to its popularity and demonstrated competitive performance. Quantum particles are employed to monitor the neighborhoods of the best solutions of each sub-swarm in order to overcome the loss of diversity problem. Quantum strategy is also used to respond to dynamic events. Moreover, economical FA along with a simpler move function is introduced in order to consume fitness evaluations more efficiently. Most of the previous approaches ignore prioritizing sub-swarms which can be advantageous. For example, sub-swarms can either be evolved sequentially, randomly or they can be prioritized via some learning-based techniques. Thus, more promising regions might be discovered at earlier evaluations. In this context, the proposed FA extension is further enhanced with such prioritizing strategies, which are based on the feedback from sub-swarms. The experiments are conducted on the well-known Moving Peaks Benchmark along with comparisons with well-known methods. The proposed FA is found as promising and competitive according to the outcomes of the comprehensive experimental study.