Firefly algorithm (FA) is recently developed nature-inspired metaheuristic based on the flashing patterns and behaviour of fireflies. Original FA was successfully applied to solve unconstrained optimization problems. This paper presents firefly algorithm to solve constrained optimization problems. For constraint handling, firefly algorithm uses certain feasibility-based rules in order to guide ...

This paper describes an object-oriented software system for continuous optimization by a modified artificial bee colony (ABC) metaheuristic. Karaboga’s ABC algorithm was successfully used on many optimization problems and there is also a corresponding program in C. We implemented a modified version in C# which is easier for maintenance since it is object-oriented and which uses threads and sign...

This paper presents an object-oriented software system that implements a cuckoo search (CS) metaheuristic for unconstrained optimization problems. Yang and Deb developed cuckoo search algorithm in MATLAB and tested it on some standard benchmark functions as well as on some engineering optimization problems where it showed promising results. We developed our algorithm in JAVA programming languag...

We call this problem of designing the appropriate metaheuristic problem for a combinatorial (optimization) problem, the metaheuristic tuning problem [1, 3, 7]. Anecdotal evidence suggests that tuning takes a major effort, i.e. [1] states that 90% of the design and testing time can be spent fine-tuning the algorithm. Although it can be easy to come up with a variety of metaheuristics, tuning the...

In this paper we present a modification of artificial bee colony (ABC) algorithm for constrained optimization problems. In nature more than one onlooker bee goes to a promising food source reported by employed bee. Our proposed modification forms a mutant solution in onlooker phase using three onlookers. This approach obtains better results than the original artificial bee colony algorithm. Our...

The multiobjective design of digital filters using spiral optimization technique is considered in this paper. This new optimization tool is a metaheuristic technique inspired by the dynamics of spirals. It is characterized by its robustness, immunity to local optima trapping, relative fast convergence and ease of implementation. The objectives of filter design include matching some desired freq...

The expanded Invasive Weed Optimization algorithm (exIWO) is an optimization metaheuristic modelled on the original IWO version created by the researchers from the University of Tehran. The authors of the present paper have extended the exIWO algorithm introducing a set of both deterministic and nondeterministic strategies of individuals’ selection. The goal of the project was to evaluate the e...