Metaheuristics (Operations Research)

We present a metaheuristic algorithm for testing software, especially web applications, that can be modelled as a state transition diagram. We formulate the testing problem as an optimization problem and use a genetic algorithm to generate test cases as sequences of events. This algorithm evolves solutions by maximizing a fitness function that is based on testing objectives such as the coverage of events, diversity of events, and continuity of events. The proposed approach includes weights that can be assigned to events. These events would lead to important features or web pages in order to ensure that test cases will be generated to cover these features. The effectiveness of the genetic algorithm is compared with that of other algorithms, namely simulated annealing and a greedy algorithm. Our experimental results show that the proposed genetic algorithm demonstrates serious promise for testing state-based software, especially web applications.

Integrating flexibly-operated carbon capture and storage (CCS) into the existing power plants has operational benefits for the future low carbon power systems. This paper proposes an improved formulation for flexible operation of carbon capture power plants (CCPPs) within the conventional economic dispatch (ED) problem. The main contribution of this work is the simplification and the practicality of the variables used for the flexible operation control of the facility. The optimal ED problem of thermal power generation portfolio with CCPPs within the mix are computed using a chaos-enhanced Cuckoo Search optimization algorithm. To test the proposed formulations, an IEEE 30 bus test system was used. The impact of varying carbon prices on the system dispatch was investigated. The results reveal the potentiality of decoupling the generation and emission outputs of the thermal power plants.

Batch processing machines are commonly used in metal working, chemical processing, and electronics manufacturing to name a few. Scheduling these machines optimally is a hard task; the number of decision variables used in the mathematical formulations grow exponentially. Consequently, commercial solvers used to solve the formulations require prohibitively long run times. Schedulers struggle to make good decisions due to the complexity of the problem. This research considers the problem of scheduling a single batch processing machine such that the total number of tardy jobs is minimized. The machine can simultaneously process several jobs as a batch as long as the machine capacity is not violated. The batch processing time is equal to the largest processing time among those jobs in the batch. Two decisions are made to schedule jobs on the batch processing machines, namely grouping jobs to form batches and sequencing the batches formed on the machines. Both the decisions are interdependent as the composition of the batch affects the processing time of the batch. The problem under study is NP-hard. Consequently, solving a mathematical formulation to find optimal solutions is computationally intensive. A greedy randomized adaptive search procedure (GRASP) is proposed to solve the problem under study with the assumption of arbitrary job sizes, arbitrary processing times and arbitrary due dates. A novel construction phase for the GRASP approach is also proposed to improve the solution quality. In addition, a path relinking procedure is also proposed for solving large-sized problems effectively. The performance of the proposed GRASP approach is evaluated by comparing its results to a commercial solver. Experimental studies suggest that the solution

The present essay synthetizes the challenges that governments and businesses face, according to their size, in Latin America and the Caribbean, so that an ambience of shared prosperity can prevail and Sustainable Development Goal #9 from the UN 2030 Agenda can be achieved. Nonetheless, the essay, which is structured upon a systemic view, presents quality education as the key factor within a net of other substantial elements for propitiating prosperity.

Recently, many studies are carried out with inspirations from ecological phenomena for developing optimization techniques. The new algorithm that is motivated by a common phenomenon in agriculture is colonization of invasive weeds. In this paper, a modified invasive weed optimization (IWO) algorithm is presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the criteria to minimize the maximum completion time (makespan), the total workload of machines and the workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve continuous optimization problems that's why the heuristic rule the Smallest Position Value (SPV) is used to convert the continuous position values to the discrete job sequences. The computational experiments show that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to find the optimal and best-known solutions on the instances studied.

Testing is crucial to ensure the quality of software systems – but testing is an expensive process, so test managers try to minimise the set of tests to run to save computing resources and speed up the testing process and analysis. One problem is that there are different perspectives on what is a good test and it is usually not possible to compare these dimensions. This is a perfect example of a multi-objective optimisation problem, which is hard — especially given the scale of the search space here. In this paper, we propose a novel hybrid algorithm to address this problem. Our method is composed of three steps: a greedy algorithm to find quickly some good solutions, a genetic algorithm to increase the search space covered and a local search algorithm to refine the solutions. We demonstrate through a large scale empirical evaluation that our method is more reliable (better whatever the time budget) and more robust (better whatever the number of dimensions considered) – in the scenario with 4 objectives and a default execution time, we are 178% better in hypervolume on average than the state-of-the-art algorithms.

— A novel algorithm to solve constrained real-parameter optimization problems, based on the Artificial Bee Colony algorithm is introduced in this paper. The operators used by the three types of bees (employed, onlooker and scout) are modified in such a way that more diverse and convenient solutions are generated. Furthermore, a dynamic tolerance control mechanism for equality constraints is added to the algorithm in order to facilitate the approach to the feasible region of the search space. Finally, two simple local search operators are applied to the best solution found so far. The algorithm, called Elitist-ABC, is tested on 18 test problems based on the experimental design proposed for the CEC'2010 competition on constrained real-parameter optimization. The results obtained are discussed and some conclusions are drawn.

Glossary Social Algorithms • Algorithm: An algorithm is a step-by-step, computational procedure or a set of rules to be followed by a computer in calculations or computing an answer to a problem. • Ant colony optimization: Ant colony optimization (ACO) is an algorithm for solving optimization problems such as routing problems using multiple agents. ACO mimics the local interactions of social ant colonies and the use of chemical messenger – pheromone to mark paths. No centralized control is used and the system evolves according to simple local interaction rules. • Bat algorithm: Bat algorithm (BA) is an algorithm for optimization, which uses frequency-tuning to mimic the basic behaviour of echolocation of microbats. BA also uses the variations of loudness and pulse emission rates and a solution vector to a problem corresponds to a position vector of a bat in the search space. Evolution of solutions follow two algorithmic equations for positions and frequencies. • Bees-inspired algorithms: Bees-inspired algorithms are a class of algorithms for optimization using the foraging characteristics of honeybees and their labour division to carry out search. Pheromone may also be used in some variants of bees-inspired algorithms. • Cuckoo Search: Cuckoo search (CS) is an optimization algorithm that mimics the brood parasitism of some cuckoo species. A solution to a problem is considered as an egg laid by a cuckoo. The evolution of solutions is carried out by Lévy flights and the similarity of solutions controlled by a switch probability. • Firefly algorithm: Firefly algorithm is an optimization inspired by the flashing patterns of tropical fireflies. The location of a firefly is equivalent to a solution vector to a problem, and the evolution of fireflies follows a nonlinear equation to simulate the attraction between fireflies of different brightness that is linked to the objective landscape of the problem. • Metaheuristic: Metaheuristic or metaheuristic algorithms are a class of optimization algorithms designed by drawing inspiration from nature. They are thus mostly nature-inspired algorithms, and examples of such metaheuristic algorithms are ant colony optimization, fire-fly algorithm, and particle swarm optimization. These algorithms are often swarm intelligence based algorithms.  Nature-Inspired computation: Nature-inspired computation is an area of computer science, concerning the development and application of nature-inspired metaheuristic algorithms for optimization, data mining, machine learning and computational intelligence.

Firefly algorithm is a nature-inspired optimization algorithm and there have been significant developments since its appearance about ten years ago. This chapter summarizes the latest developments about the firefly algorithm and its variants as well as their diverse applications. Future research directions are also highlighted.

Recently, many studies are carried out with inspirations from ecological phenomena for developing optimization techniques. The new algorithm that is motivated by a common phenomenon in agriculture is colonization of invasive weeds. In this paper, a modified invasive weed optimization (IWO) algorithm is presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the criteria to minimize the maximum completion time (makespan), the total workload of machines and the workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve continuous optimization problems that's why the heuristic rule the Smallest Position Value (SPV) is used to convert the continuous position values to the discrete job sequences. The computational experiments show that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to find the optimal and best-known solutions on the instances studied.

This Editorial article is devoted to better introduce the Mission and Vision of ICSES Transactions on Evolutionary and Metaheuristic Algorithms (ITEMA) to the community of evolutionary and swarm intelligence scientists, engineers and practitioners. Actually, the ICSES Transactions on Evolutionary and Metaheuristic Algorithms (ITEMA) is a peer-reviewed open-access publication of its kind that aims at reporting the most recent research and developments in the area of bio-inspired intelligent computation based on the principles of evolutionary as well as metaheuristic algorithms. It publishes advanced, innovative and interdisciplinary research involving the theoretical, experimental and practical aspects of the two paradigms and their hybridizations. ICSES Transactions on Evolutionary and Metaheuristic Algorithms (ITEMA) is committed to timely publication of very high-quality, peer-reviewed, original articles that advance the state-of-the art of all aspects of evolutionary computation and metaheuristics. Survey papers reviewing the state-of-the-art of timely topics will also be welcomed as well as novel and interesting applications.

We compare six metaheuristic optimization algorithms applied to solving the travelling salesman problem. We focus on three classical approaches: genetic algorithms, simulated annealing and tabu search, and compare them with three recently developed ones: quantum annealing, particle swarm optimization and harmony search. On top of that we compare all results with those obtained with a greedy 2-opt interchange algorithm. We are interested in short-term performance of the algorithms and use three criteria to evaluate them: solution quality, standard deviation of results and time needed to reach the optimum. Following the results from simulation experiments we conclude that simulated annealing and tabu search outperform newly developed approaches in short simulation runs with respect to all three criteria. Simulated annealing finds best solutions, yet tabu search has lower variance of results and converges faster.

This exploratory study proposes a new approach that utilized pre-built issues libraries in healthcare supply chain. Supply chain related issues are collected and deduced from the literature to build issues libraries. This is followed by application of group decision-making for their prioritization and defining solution requirements from doctors' perspectives. A new approach of shared decision-making is proposed by utilizing literature for developing pre-built issues libraries as an input to shared decision-making. Quick identification and resolution mean that an organisation is continually learning and moving towards excellence. It can be used as a checklist for comparison within and across organisations. Usage of open source applications such as Google Sheets and WhatsApp was utilized for geographically dispersed experts.

Task scheduling on different processors with precedence constraints is NP hard and finds a prominent place in the field of parallel computing and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. Amongst the metaheuristics, Simulated Annealing (SA) and Genetic Algorithm (GA) represent the powerful combinatorial optimization methods with corresponding strengths and weaknesses. Borrowing the respective advantages of the two paradigms, an effective combination of GA and SA called hybrid GASA has been proposed for multiprocessor task scheduling problems with precedence constraints. The bi-criteria objective function, including the weighted sum of makespan and total completion has been considered for the analysis. Comparative analysis with the help of defined performance index on the standard problems shows that the proposed hybrid GASA provides better results when compared to simple GA and SA alone in terms of solution quality.