The Weighted Set Cover Problem (SCP) is a popular optimization problem that has been applied to different industrial applications, including scheduling, manufacturing, service planning and location problems. It consists in to find low cost solutions covering a set of requirements or needs. In this paper, we solve the SCP using a recent nature inspired algorithm: Invasive Weed Optimization (IWO)...

This paper presents an application of the Ant colony optimization metaheuristic to the job shop scheduling problem. A pheromone alteration strategy which improves the basic ant system by utilizing the behaviour of artificial ants to perform local search is introduced. Experiments using well-known benchmark JSSP problems show that this approach improves on the performance obtained by the basic a...

Ant Colony Optimization (ACO) a nature-inspired metaheuristic algorithm has been successfully applied in the traveling salesman problem (TSP) and a variety of combinatorial problems. In fact, ACO can effectively fit to discrete optimization problems and exploit pre-knowledge of the problems for a faster convergence. We present an improved version of ACO with a kind of Genetic semi-random-restar...

The paper presents a Genetic Algorithm (GA) approach for solving constrained reliability redundancy optimization of general systems. The advanced GA technique uses a dynamic adaptive penalty function to consider the infeasible solutions also and guides the search to optimal or near optimal solution. The penalty technique is applied to keep a certain amount of infeasible solutions in each genera...

This paper reviews modern genetic algorithm based approaches for solving job shop scheduling problems Genetic algorithms represent one of the most popular and mostly used metaheuristic methods applied for solving many optimization problems within last few decades. Job shop scheduling represents one of the hardest combinatorial optimization problems where number of possible schedules drastically...

Automatic test data generation is required to generate test cases dynamically for a specific software program. Manual generation of test data is too tedious and a time consuming task. This paper proposes a technique using Intelligent Water Drop (IWD) for automatic generation of test data. Correctly generated test data helps in reducing the effort while testing the software. This paper discusses...

Metaheuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimization problems. More than a dozen of major metaheuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrid of metaheuristics. This paper intends to provide an overview of nature-inspired metaheuristic algorithms, from a brief hi...

This paper deals with the specification of a Decision Support System (DSS) that has to manage the flow of goods and the business transactions between a port and a dry port. The paper investigates the case of the broader area of Trieste port and specifies the DSS that manages the import flows of freights between dry port and seaport. An integrated approach is designed for the tactical level deci...

The paper describes an application of Parallel Simulated Annealing (PSA) for solving one of the most studied NP-hard optimization problems: Graph Coloring Problem (GCP). Synchronous master-slave model with periodic solution update is being used. The paper contains description of the method, recommendations for optimal parameters settings and summary of results obtained during algorithm’s evalua...

Software testing is one of the most labor-intensive and expensive phase of the software development life cycle. Software testing includes test case generation and test suite optimization that has a strong impact on the effectiveness and efficiency of software testing. Over the past few decades, there has been active research to automate the process of test case generation but the attempts have ...