Differential evolution with Pareto tournament for the multi-objective next release problem

Chaves-González, José M.; Pérez-Toledano, Miguel A.

doi:10.1016/j.amc.2014.11.093

Cited by 24 publications

(14 citation statements)

References 23 publications

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“…Chavez et al [17] proposed a bi-objective algorithm (TLBO). A multi-objective differential evolution (DE) technique was developed considering the Pareto tournaments (MODE) [23].…”

Section: Related Workmentioning

confidence: 99%

Software requirement selection using a combined multi‐objective optimisation technique

et al. 2022

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The optimal requirements selection set aims primarily at careful search for the best requirements set of the next release of software during development process. This procedure is widely defined as the next release problem (NRP), which is also classified as NP-hard dilemma. Several techniques, in literature, have been proposed to tackle NRP. However, in real examples, the earlier studies still immature as NRP still suffers interactions and restrictions that makes the problem more complicated. Although few interesting works have been presented, yet NRP, based on our study, could be further investigated and effectively tackled. In this research, therefore, NRP is devised as a multiobjective optimisation problem. Two clashing objectives (satisfaction and cost) and two constraints (interactions forms) are formulated. To tackle NRP effectively, a new hybrid genetic and artificial bee colony algorithm (HGABC) is introduced. HGABC combines features of genetic and artificial bee colony algorithms. Experimental study, using case studies and three criteria, have been conducted to show HGABC's power of generating non-dominated effective Pareto solutions versus the state-of-the-art algorithms. Results indicate that HGABC does not just outperform its rivals, yet also gives better Pareto solutions in terms of diversity and quality for almost all the instances of this problem.

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“…Chavez et al [17] proposed a bi-objective algorithm (TLBO). A multi-objective differential evolution (DE) technique was developed considering the Pareto tournaments (MODE) [23].…”

Section: Related Workmentioning

confidence: 99%

Software requirement selection using a combined multi‐objective optimisation technique

et al. 2022

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“…Bagnall et al [3] compared greedy randomized adaptive search procedure (GRASP), hill climber and simulated annealing (SA) algorithms in solving different sizes of the NRP; and they found that the SA outperformed both the GRASP and the hill climber especially with large scale NRP instances. Glauber et al [5] investigated solving the large scale NRP using two metaheuristic algorithms, the ant colony optimization (ACO) and the particle swarm optimization (PSO). The researchers aimed to identify which metaheuristic algorithm is more suitable for handling the large scale NRP.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, their experiments showed that ACO can achieve same performance of PSO for small sizes of the NRP. However, the work of [3] and [5] did not consider the interactions among the requirements. Three research studies considered the interactions among the requirements [4], [11], [12].…”

Section: Related Workmentioning

confidence: 99%

“…The algorithm of the OBPSO to solve NRP can be summarized as follows: -Update the particle's velocity vector Vi according to (5) and (6).…”

Section: ) Original Bpsomentioning

confidence: 99%

“…Maximizing the total satisfaction of the customers, and minimizing the company's development cost/effort, are two conflicting objectives that categorize the NRP as a multiobjective optimization problem which is best tackled by metaheuristic algorithms [4]. Some researchers tackled the NRP problem using PSO [5]- [9], ant colony optimization (ACO) [5], [10]- [12], genetic algorithms (GA) [5], [16], [13]- [15]. Some previous research proved that the original BPSO (OBPSO) surpasses both of the ant colony and the GA [3] in solving the NRP, especially the large scale NRP.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Greedy Binary Particle Swarm Optimization for multi-Objective Constrained Next Release Problem

Hamdy¹,

Mohamed²

2019

IJMLC

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Software companies, that adopt agile methodologies in the development of a large and complex software product, encounter the problem of selecting the subset of requirements to be included in the next release of the product. This problem is known as the next release problem (NRP). NRP is identified as an NP-hard problem as it involves a set of conflicting objectives that need to be addressed. These objectives are as follows: (1) maximizing the customer satisfaction taking into consideration that each customer has an importance level to the company, and (2) minimizing the development cost such that it does not exceed the allocated budget. Furthermore, the requirements have dependency and precedence relationships, and each requirement has a priority for each customer according to the customer's needs. Therefore, metaheuristic algorithms are good candidate for tackling this problem. This paper proposes a hybrid approach to address the multi-objective constrained NRP. The proposed approach is based on adapting an improved binary particle swarm optimization (IBPSO) algorithm. Additionally, a greedy methodology was utilized for swarm initialization to seed the swarm with good solutions. Experimental results, of over six small and large NRP instances, demonstrated that the proposed approach converges much faster to solutions better than the ones discovered by the original binary PSO.

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Software requirement optimization using a fuzzy artificial chemical reaction optimization algorithm

2018

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Differential evolution with Pareto tournament for the multi-objective next release problem

Cited by 24 publications

References 23 publications

Software requirement selection using a combined multi‐objective optimisation technique

Software requirement selection using a combined multi‐objective optimisation technique

Greedy Binary Particle Swarm Optimization for multi-Objective Constrained Next Release Problem

Software requirement optimization using a fuzzy artificial chemical reaction optimization algorithm

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