Add to ORKGThis paper carries out the performance study of Harmony Search Algorithm (HSA) employing some standard test functions [1]. Unconstrained test functions like Sphere, Rastrigin, Ackley and Rosenbrock functions are chosen for this work. The efficacy of harmony search algorithm is justified by comparing it with Genetic Algorithm (GA). Further improved versions of harmony search algorithm can be used to test these benchmark functions and obtain better results. Constrained optimization problems can also be considered in future
In this paper, we propose an improved harmony search algorithm named LHS with three key features: (i...
Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization p...
This paper present a successful method to tune the parameters of the harmony search algorithm, which...
Harmony search algorithm is the recently developed metaheuristic in the last decade. It mimics the b...
Harmony search algorithm (HSA) is relatively considered as one of the most recent metaheuristic algo...
In the last two decades, a number of optimization techniques have been developed for solving various...
A metaheuristic algorithm, called Harmony Search is quite highly applied in optimizing parameters in...
A Harmony Search (HS) algorithm is a population based-meta-heuristics approach that is superior in ...
The theoretical analysis of evolutionary algorithms is believed to be very important for understandi...
The Harmony Search (HS) algorithm appeared around the year 2000 and it now has a substantial literat...
AbstractSince the Harmony Search Algorithm (HSA) was first introduced in 2001, it has drawn a world-...
The Harmony Search Algorithm (HSA) is one of the most well-known techniques in the field of soft com...
Harmony search algorithm (HS) is a new metaheuristic algorithm which is inspired by a process involv...
This paper presents a comprehensive overview of the development of the harmony search (HS) algorithm...
Harmony search (HS) was introduced in 2001 as a heuristic population-based optimisation algorithm. S...
In this paper, we propose an improved harmony search algorithm named LHS with three key features: (i...
Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization p...
This paper present a successful method to tune the parameters of the harmony search algorithm, which...
Harmony search algorithm is the recently developed metaheuristic in the last decade. It mimics the b...
Harmony search algorithm (HSA) is relatively considered as one of the most recent metaheuristic algo...
In the last two decades, a number of optimization techniques have been developed for solving various...
A metaheuristic algorithm, called Harmony Search is quite highly applied in optimizing parameters in...
A Harmony Search (HS) algorithm is a population based-meta-heuristics approach that is superior in ...
The theoretical analysis of evolutionary algorithms is believed to be very important for understandi...
The Harmony Search (HS) algorithm appeared around the year 2000 and it now has a substantial literat...
AbstractSince the Harmony Search Algorithm (HSA) was first introduced in 2001, it has drawn a world-...
The Harmony Search Algorithm (HSA) is one of the most well-known techniques in the field of soft com...
Harmony search algorithm (HS) is a new metaheuristic algorithm which is inspired by a process involv...
This paper presents a comprehensive overview of the development of the harmony search (HS) algorithm...
Harmony search (HS) was introduced in 2001 as a heuristic population-based optimisation algorithm. S...
In this paper, we propose an improved harmony search algorithm named LHS with three key features: (i...
Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization p...
This paper present a successful method to tune the parameters of the harmony search algorithm, which...