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...

We proposed a population-based metaheuristic called the spy algorithm for solving optimization problems and evaluated its performance. The design of our ensures benefit exploration exploitation as well cooperative non-cooperative searches in each iteration. compared with genetic algorithm, improved harmony search, particle swarm on set non-convex functions that focus accuracy, ability detecting...

In this paper, we present a new Artificial Bee Colony algorithm to solve the non-unicost Set Covering Problem. The Artificial Bee Colony algorithm is a recent metaheuristic technique based on the intelligent foraging behavior of honey bee swarm. Computational results show that Artificial Bee Colony algorithm is competitive in terms of solution quality with other metaheuristic approaches for the...

This paper proposes a mixed integer programming model to solve a non-identical parallel machine (NIPM) scheduling with sequence-dependent set-up times and human resiliency engineering. The presented mathematical model is formulated to consider human factors including Learning, Teamwork and Awareness. Moreover, processing time of jobs are assumed to be non-deterministic and dependent to their st...

this paper addresses the vehicle routing problem with delivery time cost. this problem aims to find a set of routes of minimal total costs including the travelling cost and delivery time cost, starting and ending at the depot, in such a way that each customer is visited by one vehicle given the capacity of the vehicle to satisfy a specific demand. in this research, a hybrid metaheuristic approa...

We consider the application of a new metaheuristic technique that we have proposed, known as PROBE (Population Reinforced Optimization Based Exploration), to the multiconstraint knapsack problem (MKP). In the current section, we begin by giving a brief introduction to PROBE; we then briefly review other metaheuristic approaches for the MKP. In Section 2, we describe our PROBE approach for the M...

Metaheuristic algorithms will gain more and more popularity in the future as optimization problems are increasing in size and complexity. In order to record experiences and allow project to be replicated, a standard process as a methodology for designing and implementing metaheuristic algorithms is necessary. To the best of the authors’ knowledge, no methodology has been proposed in literature ...

Nature is a great source of inspiration for solving complex problems in networks. It helps to find the optimal solution. Metaheuristic algorithm is one of the nature-inspired algorithm which helps in solving routing problem in networks. The dynamic features, changing of topology frequently and limited bandwidth make the routing, challenging in MANET. Implementation of appropriate routing algori...

Examination timetabling problems are often solved by a two-phase procedure combining a sequential construction heuristic with a metaheuristic improvement search. There can be many combinations of pairing candidate construction heuristics with a metaheuristic. Different pairings are known to produce solutions of varying quality. In this paper we propose a Case Based Reasoning methodology for sel...

An improved method for adaptive noise canceller (ANC) is proposed for noisy speech signal in case of random noise. In this approach, ANC is implemented through four different metaheuristic techniques. A comparative study of the performance of various metaheuristic techniques such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuckoo Search (CS) and Modified Cuckoo Search (MC...