AbstractHarmony Search metaheuristic is successfully used in several applications of science and engineering. However, its effectiveness in solving multiobjective optimization problems using the concepts of Pareto optimality, remains unproved. This paper presents two proposals of the Harmony Search metaheuristic for multiobjective optimization, using the ZDT functions as a test bed. Performance metrics for experimental results show that the proposals are competitive even when compared to NSGA-II evolutionary algorithm
The performance of the Harmony Search (HS) algorithm is highly dependent on the parameter settings a...
Many optimisation problems are dynamic in the sense that changes occur during the optimisation proce...
AbstractMany optimisation problems are dynamic in the sense that changes occur during the optimisati...
AbstractHarmony Search metaheuristic is successfully used in several applications of science and eng...
A metaheuristic algorithm, called Harmony Search is quite highly applied in optimizing parameters in...
Harmony search algorithm (HS) is a new metaheuristic algorithm which is inspired by a process involv...
Harmony search algorithm is the recently developed metaheuristic in the last decade. It mimics the b...
The theoretical analysis of evolutionary algorithms is believed to be very important for understandi...
In this work, a new version of the Harmony Search algorithm for solving multi-objective optimization...
Abstract—This paper proposes two hybrid optimization methods based on Harmony Search algorithm (HS) ...
A Harmony Search (HS) algorithm is a population based-meta-heuristics approach that is superior in ...
Harmony Search is a metaheuristic technique for optimizing problems involving sets of continuous or ...
Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization p...
To solve the comprehensive multiobjective optimization problem, this study proposes an improved meta...
Dynamic optimization problems present great challenges to the research community because their param...
The performance of the Harmony Search (HS) algorithm is highly dependent on the parameter settings a...
Many optimisation problems are dynamic in the sense that changes occur during the optimisation proce...
AbstractMany optimisation problems are dynamic in the sense that changes occur during the optimisati...
AbstractHarmony Search metaheuristic is successfully used in several applications of science and eng...
A metaheuristic algorithm, called Harmony Search is quite highly applied in optimizing parameters in...
Harmony search algorithm (HS) is a new metaheuristic algorithm which is inspired by a process involv...
Harmony search algorithm is the recently developed metaheuristic in the last decade. It mimics the b...
The theoretical analysis of evolutionary algorithms is believed to be very important for understandi...
In this work, a new version of the Harmony Search algorithm for solving multi-objective optimization...
Abstract—This paper proposes two hybrid optimization methods based on Harmony Search algorithm (HS) ...
A Harmony Search (HS) algorithm is a population based-meta-heuristics approach that is superior in ...
Harmony Search is a metaheuristic technique for optimizing problems involving sets of continuous or ...
Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization p...
To solve the comprehensive multiobjective optimization problem, this study proposes an improved meta...
Dynamic optimization problems present great challenges to the research community because their param...
The performance of the Harmony Search (HS) algorithm is highly dependent on the parameter settings a...
Many optimisation problems are dynamic in the sense that changes occur during the optimisation proce...
AbstractMany optimisation problems are dynamic in the sense that changes occur during the optimisati...