Dominance-based Approaches Research Articles

Fusion prediction strategy-based dynamic multi-objective sparrow search algorithm

Solving dynamic multi-objective optimization problems with time-varying Pareto front (PF) or Pareto set (PS) is a challenging task. Such problems require algorithms to react to environmental changes and efficiently track optimal solutions. For this purpose, a dynamic multi-objective sparrow search algorithm (SSA) with fusion prediction strategy, based on difference model and kernel extreme learning machine (DMOSSA-FPS), is proposed. Given the diversity of change characteristics, a single prediction model is insufficient. Therefore, based on the historical information of the population, a difference model and a kernel extreme learning machine are integrated for PS prediction. The former is used to predict the solutions of some individuals under approximate linear changes and the latter is employed for nonlinear predictions. In a new environment, the combined predictions increase the diversity of the initial population. Additionally, a new static optimizer is proposed, which combines decomposition- and dominance-based approaches to constitute a new individual screening mechanism. Then the optimization mode of SSA is introduced to enhance both algorithmic diversity and convergence rate. The experimental results on the DF test suite demonstrate that, compared with several other advanced algorithms, DMOSSA-FPS exhibits stronger convergence and robustness.

Flexible QoS-aware services composition for service computing environments

The rapid development of service computing environments has led to an increase in the number of functionally equivalent services that differ according to their QoS (Quality of Service) attributes values. Accordingly, a challenging issue in the context of services composition is how to select the most appropriate candidate services for each user’s requirements such that the obtained composite service satisfies the user’s non-functional requirements in terms of global QoS constraints. In this paper, a Flexible QoS-aware Services Composition (FQSC) algorithm is proposed to increase the composition feasibility and reduce the composition time, while the composition optimality is maintained as high as possible. The proposed algorithm deals with the limitations of Pareto dominance-based and QoS decomposition-based services composition approaches. Indeed, the number of compositions increases exponentially with the number of services and QoS attributes in the case of Pareto dominance-based approaches, whereas the QoS decomposition-based approaches can lead to a decrease in the composition feasibility when the user’s global QoS constraints are highly restrictive. The simulation results show that the proposed approach is effective compared to other services composition approaches in terms of composition time, optimality and search space.

A parallel MOEA with criterion-based selection applied to the Knapsack Problem

In this paper, we propose a parallel multiobjective evolutionary algorithm called Parallel Criterion-based Partitioning MOEA (PCPMOEA), with an application to the Multiobjective Knapsack Problem (MOKP). The suggested search strategy is based on a periodic partitioning of potentially efficient solutions, which are distributed to multiple multiobjective evolutionary algorithms (MOEAs). Each MOEA is dedicated to a sole objective, in which it combines both criterion-based and dominance-based approaches. The suggested algorithm addresses two main sub-objectives: minimizing the distance between the current non-dominated solutions and the ideal point, and ensuring the spread of the potentially efficient solutions. Experimental results are included, where we assess the performance of the suggested algorithm against the above mentioned sub-objectives, compared with state-of-the-art results using well-known multi-objective metaheuristics.

A cost-efficient framework for finding prospective customers based on reverse skyline queries

Analyzing customers’ information for marketing insights is critical for the company to remain invincible in increasingly fierce market competition. Rank-based and dominance-based customer selections are widely used for the market analysis to find prospective customers. However, rank-based solutions required companies to define weight vectors, which is not practical in real applications. Dominance-based approaches (e.g., reverse skyline queries) had problems of a high overhead and the poor progressiveness. In this paper, we propose a cost-efficient framework to find prospective customers for a target product. We first target on the most prospective customers for whom no better product exists compared with the target product. We formulate the problem based on reverse skyline queries, and propose a new algorithm that significantly reduces the query cost by pruning unqualified customers without any false positive and identifies reverse skyline points as early as possible based on decision region and effect region. We then further target on arbitrary k prospective customers and formulate the problem as top-k reverse skyline (top-k RS) queries. We extend the notion of reverse skyline to reverse skyline order in order to support arbitrary k and group-based promotions. We evaluate our framework with extensive experiments, and our results demonstrate that our framework has the promising results for reverse skyline queries, and can efficiently support top-k RS queries.

Problem features vs. algorithm performance on rugged multi-objective combinatorial fitness landscapes

In this paper, we attempt to understand and to contrast the impact of performance of randomized search heuristics for black-box multiobjective combinatorial optimization problems. At first, we measure the performance of two conventional dominance-based approaches with unbounded archive on a benchmark of enumerable binary optimization problems with tunable ruggedness, objective space dimension, and objective correlation (ρMNK-landscapes). Precisely, we investigate the expected runtime required by a global evolutionary optimization algorithm with an ergodic variation operator (GSEMO) and by a neighborhood-based local search heuristic (PLS), to identify a -approximation of the Pareto set. Then, we define a number of problem features characterizing the fitness landscape, and we study their intercorrelation and their association with algorithm runtime on the benchmark instances. At last, with a mixed-effects multi-linear regression we assess the individual and joint effect of problem features on the performance of both algorithms, within and across the instance classes defined by benchmark parameters. Our analysis reveals further insights into the importance of ruggedness and multi-modality to characterize instance hardness for this family of multi-objective optimization problems and algorithms.

Problem Features versus Algorithm Performance on Rugged Multiobjective Combinatorial Fitness Landscapes.

In this article, we attempt to understand and to contrast the impact of problem features on the performance of randomized search heuristics for black-box multiobjective combinatorial optimization problems. At first, we measure the performance of two conventional dominance-based approaches with unbounded archive on a benchmark of enumerable binary optimization problems with tunable ruggedness, objective space dimension, and objective correlation ([Formula: see text]MNK-landscapes). Precisely, we investigate the expected runtime required by a global evolutionary optimization algorithm with an ergodic variation operator (GSEMO) and by a neighborhood-based local search heuristic (PLS), to identify a ([Formula: see text]approximation of the Pareto set. Then, we define a number of problem features characterizing the fitness landscape, and we study their intercorrelation and their association with algorithm runtime on the benchmark instances. At last, with a mixed-effects multilinear regression we assess the individual and joint effect of problem features on the performance of both algorithms, within and across the instance classes defined by benchmark parameters. Our analysis reveals further insights into the importance of ruggedness and multimodality to characterize instance hardness for this family of multiobjective optimization problems and algorithms.

A Survey of Multiobjective Evolutionary Algorithms based on Decomposition

Decomposition is a well-known strategy in traditional multiobjective optimization. However, the decomposition strategy was not widely employed in evolutionary multiobjective optimization until Zhang and Li proposed multiobjective evolutionary algorithm based on decomposition (MOEA/D) in 2007. MOEA/D proposed by Zhang and Li decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them in a collaborative manner using an evolutionary algorithm (EA). Each subproblem is optimized by utilizing the information mainly from its several neighboring subproblems. Since the proposition of MOEA/D in 2007, decomposition-based MOEAs have attracted significant attention from the researchers. Investigations have been undertaken in several directions, including development of novel weight vector generation methods, use of new decomposition approaches, efficient allocation of computational resources, modifications in the reproduction operation, mating selection and replacement mechanism, hybridizing decomposition- and dominance-based approaches, etc. Furthermore, several attempts have been made at extending the decomposition-based framework to constrained multiobjective optimization, many-objective optimization, and incorporate the preference of decision makers. Additionally, there have been many attempts at application of decomposition-based MOEAs to solve complex real-world optimization problems. This paper presents a comprehensive survey of the decomposition-based MOEAs proposed in the last decade.

A comparative study of preference dominance-based approaches for selection of industrial robots

A B S T R A C T A R T I C L E I N F O In the modern era of highly mechanized technologies, manufacturing organizations are now extensively using different kinds of industrial robots for performing complicated and perilous tasks with superior levels of accuracy. The major role of robotic technology within manufacturing organizations is to amalgamate design, manufacturing and management planning activities into a flexible system for improving production lines with minimum manufacturing cost involvement. However, the pre-implementation, implementation, and post-implementation phases of robotic technologies are the foremost issues associated with the selection and rationalization of robotic investments, which is based on a thorough review and exploration of various alternative robots and their mutually conflicting performance measures. Evaluating alternative robots in the presence of multiple conflicting attributes often makes the selection task very complex. This paper focuses on the application feasibilities of two preference dominance-based multi-attribute decision-making (MADM) approaches, namely evaluation of mixed data (EVAMIX) and extended preference ranking organization method for enrichment evaluation II (EXPROM2) whilst selecting the best alternative robots within given manufacturing environments. Using these two methods, a list of all the feasible alternatives from the best to the worst suitable robot is obtained by taking into account different robot selection attributes. The ranking performances of these methods are also compared with those of the past researchers, using four performance tests.

New Constraint-Handling Method for Multi-Objective and Multi-Constraint Evolutionary Optimization

A new constraint-handling method based on Pareto-optimality and niching concepts for multi-objective multi-constraint evolutionary optimization is proposed. The proposed method does not require any constants to be tuned for constraint-handling. In addition, the present method does not use the weighted-sum of constraints and thus does not require tuning of weight coefficients and is efficient even when all individuals in the initial population are infeasible or the amount of violation of each constraint is significantly different. The proposed approach is demonstrated to be remarkably more robust than the dynamic penalty approach and other dominance-based approaches through the optimal design of a welded beam and conceptual design optimization of a two-stage-to-orbit spaceplane.