Evolutionary Multi-Objective Optimization for Web Service Location Allocation Problem
01 Mar 2021-IEEE Transactions on Services Computing (Institute of Electrical and Electronics Engineers (IEEE))-Vol. 14, Iss: 2, pp 458-471
TL;DR: This paper develops a new PSO-based algorithm to provide a set of trade-off solutions for Web Service Location Allocation Problem and shows that the new algorithm can provide a more diverse range of solutions than the compared three well known multi-objective optimization algorithms.
Abstract: With the ever increasing number of functionally similar web services being available on the Internet, the market competition is becoming intense. Web service providers (WSPs) realize that good Quality of Service (QoS) is a key of business success and low network latency is a critical measurement of good QoS. Because network latency is related to location, a straightforward way to reduce network latency is to allocate services to proper locations. However, Web Service Location Allocation Problem (WSLAP) is a challenging task since there are multiple objectives potentially conflicting with each other and the solution search space has a combinatorial nature. In this paper, we consider minimizing the network latency and total cost simultaneously and model the WSLAP as a multi-objective optimization problem. We develop a new PSO-based algorithm to provide a set of trade-off solutions. The results show that the new algorithm can provide a more diverse range of solutions than the compared three well known multi-objective optimization algorithms. Moreover, the new algorithm performs better especially on large problems.
Citations
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TL;DR: A simple and efficient two-phase framework, named ToP, is proposed in this paper to enhance current CMOEAs’ performance on DOC, the first attempt to consider both the decision and objective constraints simultaneously in the design of artificial CMOPs.
Abstract: Constrained multiobjective optimization problems (CMOPs) are frequently encountered in real-world applications, which usually involve constraints in both the decision and objective spaces. However, current artificial CMOPs never consider constraints in the decision space (i.e., decision constraints) and constraints in the objective space (i.e., objective constraints) at the same time. As a result, they have a limited capability to simulate practical scenes. To remedy this issue, a set of CMOPs, named DOC, is constructed in this paper. It is the first attempt to consider both the decision and objective constraints simultaneously in the design of artificial CMOPs. Specifically, in DOC, various decision constraints (e.g., inequality constraints, equality constraints, linear constraints, and nonlinear constraints) are collected from real-world applications, thus making the feasible region in the decision space have different properties (e.g., nonlinear, extremely small, and multimodal). On the other hand, some simple and controllable objective constraints are devised to reduce the feasible region in the objective space and to make the Pareto front have diverse characteristics (e.g., continuous, discrete, mixed, and degenerate). As a whole, DOC poses a great challenge for a constrained multiobjective evolutionary algorithm (CMOEA) to obtain a set of well-distributed and well-converged feasible solutions. In order to enhance current CMOEAs’ performance on DOC, a simple and efficient two-phase framework, named ToP, is proposed in this paper. In ToP, the first phase is implemented to find the promising feasible area by transforming a CMOP into a constrained single-objective optimization problem. Then in the second phase, a specific CMOEA is executed to obtain the final solutions. ToP is applied to four state-of-the-art CMOEAs, and the experimental results suggest that it is quite effective.
172 citations
Cites background from "Evolutionary Multi-Objective Optimi..."
...Many real-world applications can be formulated as CMOPs, such as the Web service location allocation [1], the risk-constrained energy and reserve procurement [2], the optimal scheduling in microgrids [3], the optimal demand response strategies to mitigate oligopolistic behavior [4], and the deployment optimization of near space communication [5]....
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TL;DR: A dynamic selection preference-assisted constrained multiobjective differential evolutionary (DE) algorithm that exhibits superior or at least competitive performance, in comparison with other well-established methods.
Abstract: Solving constrained multiobjective optimization problems brings great challenges to an evolutionary algorithm, since it simultaneously requires the optimization among several conflicting objective functions and the satisfaction of various constraints. Hence, how to adjust the tradeoff between objective functions and constraints is crucial. In this article, we propose a dynamic selection preference-assisted constrained multiobjective differential evolutionary (DE) algorithm. In our approach, the selection preference of each individual is suitably switching from the objective functions to constraints as the evolutionary process. To be specific, the information of objective function, without considering any constraints, is extracted based on Pareto dominance to maintain the convergence and diversity by exploring the feasible and infeasible regions; while the information of constraint is used based on constrained dominance principle to promote the feasibility. Then, the tradeoff in these two kinds of information is adjusted dynamically, by emphasizing the utilization of objective functions at the early stage and focusing on constraints at the latter stage. Furthermore, to generate the promising offspring, two DE operators with distinct characteristics are selected as components of the search algorithm. Experiments on four test suites including 56 benchmark problems indicate that the proposed method exhibits superior or at least competitive performance, in comparison with other well-established methods.
47 citations
TL;DR: This paper surveys 717 papers published between 2009 and 2019 from 36 venues in seven repositories, and selects 95 prominent studies, through which five important but overlooked issues in the area are identified, to codify a methodological guidance for selecting and using evaluation methods in different SBSE scenarios.
Abstract: With modern requirements, there is an increasing tendency of considering multiple objectives/criteria simultaneously in many Software Engineering (SE) scenarios. Such a multi-objective optimization scenario comes with an important issue - how to evaluate the outcome of optimization algorithms, which typically is a set of incomparable solutions (i.e., being Pareto nondominated to each other). This issue can be challenging for the SE community, particularly for practitioners of Search-Based SE (SBSE). On one hand, multi-objective optimization could still be relatively new to SE/SBSE researchers, who may not be able to identify the right evaluation methods for their problems. On the other hand, simply following the evaluation methods for general multi-objective optimization problems may not be appropriate for specific SBSE problems, especially when the problem nature or decision maker's preferences are explicitly/implicitly known. This has been well echoed in the literature by various inappropriate/inadequate selection and inaccurate/misleading use of evaluation methods. In this paper, we first carry out a systematic and critical review of quality evaluation for multi-objective optimization in SBSE. We survey 717 papers published between 2009 and 2019 from 36 venues in seven repositories, and select 95 prominent studies, through which we identify five important but overlooked issues in the area. We then conduct an in-depth analysis of quality evaluation indicators/methods and general situations in SBSE, which, together with the identified issues, enables us to codify a methodological guidance for selecting and using evaluation methods in different SBSE scenarios.
39 citations
Cites background or methods from "Evolutionary Multi-Objective Optimi..."
...For example, preferring knee points yet using IGD in [32], [83]; preferring knee points yet using GD and CI in [30], [105]; and preferring extreme solutions yet using HV and IGD in [121], [124]....
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...For example, some studies set it to the worst value obtained for each objective during all runs [29], [70], [87], [98], [115], [116], [121]; some did it to precisely the boundaries of the optimization problem [26], [47], [133]; some did it to the nadir point of the Pareto front [96]....
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...in [4], [10], [28], [32], [34], [36], [45], [75], [83], [84], [116], [121])....
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...4: [125] [124] [121] [18] Log Template Identification Optimize, e....
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..., used in [21], [26], [29], [32], [47], [87], [93], [96], [98], [115], [116], [121], [133]....
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TL;DR: This article studies a new type of composite application deployment problem that jointly considers both the performance optimization and budget control in multi-cloud at the global scale, and proposes a hybrid GA-based approach, featuring new design of domain-tailored service clustering, repair algorithm, solution representation, population initialization, and genetic operators.
Abstract: Enterprise application providers are increasingly moving their workloads to the cloud for technical and economic benefits. Multi-cloud environment makes it possible to orchestrate multiple cloud resources. With the increasing number of available cloud resources provided by multiple cloud providers at different locations with different prices, application providers face the challenge to select proper cloud resources to deploy their applications in the form of a workflow of component service units. Existing studies usually consider minimizing execution time or/and deployment cost. From the perspective of application providers, however, they also pay huge attention to application response time, including particularly network latency between deployed services and users. Meanwhile, application deployment is often subject to stringent budgetary control to ensure financial viability. This article studies a new type of composite application deployment problem that jointly considers both the performance optimization and budget control in multi-cloud at the global scale. To find solutions with minimal response time without running into the risk of over-spending, we propose a hybrid GA-based approach, featuring new design of domain-tailored service clustering, repair algorithm, solution representation, population initialization, and genetic operators. Extensive experiments using the real-world dataset demonstrate that our proposed hybrid GA approach outperforms some recently proposed approaches.
36 citations
Cites background or methods from "Evolutionary Multi-Objective Optimi..."
...mance and cost, there are some research works searching for the Pareto front, in which each solution represents a unique trade-off deployment plan [14], [15]....
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...We refer to [15] and consider a set of user centres U 1⁄4 fU0; ....
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TL;DR: The proposed dual-population multi-objective optimization evolutionary algorithm can make good use of its secondary population and alternative between evolution and degeneration according to the state of the secondary population to provide better information for the main population.
30 citations
References
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TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Abstract: Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (where M is the number of objectives and N is the population size); (2) their non-elitism approach; and (3) the need to specify a sharing parameter. In this paper, we suggest a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN/sup 2/) computational complexity is presented. Also, a selection operator is presented that creates a mating pool by combining the parent and offspring populations and selecting the best N solutions (with respect to fitness and spread). Simulation results on difficult test problems show that NSGA-II is able, for most problems, to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the Pareto-archived evolution strategy and the strength-Pareto evolutionary algorithm - two other elitist MOEAs that pay special attention to creating a diverse Pareto-optimal front. Moreover, we modify the definition of dominance in order to solve constrained multi-objective problems efficiently. Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective, seven-constraint nonlinear problem, are compared with another constrained multi-objective optimizer, and the much better performance of NSGA-II is observed.
37,111 citations
"Evolutionary Multi-Objective Optimi..." refers methods in this paper
...Solutions from BPSO and NSGA-II suggest that the proposed multi-objective approaches suit the problem well....
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...The dashed-vertical lines mark the missing solutions from Reciprocal function. compare its performance with BNSPSO, BPSO from our previous research [11], and NSGA-II from [12]....
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...However, we also found a disadvantage in both BPSO and NSGA-II....
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...Coello et al. [24] study several multi-objective algorithms, NSGA-II, MicroGA [26] and MOPSO, and shows that MOPSO is the most capable of generating the best set of non-dominated solutions close to the true Pareto front with low computational cost....
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...Lastly, we conduct an experiment considering the overall performance of a BMOPSOCDwith a dynamic rounding function in comparison with three other algorithms: PSO, BNSPSO andNSGA-II (see [12])....
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TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
Abstract: Decomposition is a basic strategy in traditional multiobjective optimization. However, it has not yet been widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them simultaneously. Each subproblem is optimized by only using information from its several neighboring subproblems, which makes MOEA/D have lower computational complexity at each generation than MOGLS and nondominated sorting genetic algorithm II (NSGA-II). Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjective optimization problems. It has been shown that MOEA/D using objective normalization can deal with disparately-scaled objectives, and MOEA/D with an advanced decomposition method can generate a set of very evenly distributed solutions for 3-objective test instances. The ability of MOEA/D with small population, the scalability and sensitivity of MOEA/D have also been experimentally investigated in this paper.
6,657 citations
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...MOEA/D [21] decomposes amulti-objective problem into a number of scalar optimization subproblems and optimizes them simultaneously....
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12 Oct 1997
TL;DR: The paper reports a reworking of the particle swarm algorithm to operate on discrete binary variables, where trajectories are changes in the probability that a coordinate will take on a zero or one value.
Abstract: The particle swarm algorithm adjusts the trajectories of a population of "particles" through a problem space on the basis of information about each particle's previous best performance and the best previous performance of its neighbors. Previous versions of the particle swarm have operated in continuous space, where trajectories are defined as changes in position on some number of dimensions. The paper reports a reworking of the algorithm to operate on discrete binary variables. In the binary version, trajectories are changes in the probability that a coordinate will take on a zero or one value. Examples, applications, and issues are discussed.
4,478 citations
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...To address discrete problems, Kennedy and Eberhart developed a binary PSO [23]....
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TL;DR: An approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions and indicates that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems.
Abstract: This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. Unlike other current proposals to extend PSO to solve multiobjective optimization problems, our algorithm uses a secondary (i.e., external) repository of particles that is later used by other particles to guide their own flight. We also incorporate a special mutation operator that enriches the exploratory capabilities of our algorithm. The proposed approach is validated using several test functions and metrics taken from the standard literature on evolutionary multiobjective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems.
3,474 citations
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...[24] study several multi-objective algorithms, NSGA-II, MicroGA [26] and MOPSO, and shows that MOPSO is the most capable of generating the best set of non-dominated solutions close to the true Pareto front with low computational...
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...The mutation operator (Line 15) changes the value of each dimension of an individual according to a nonlinear function [24]....
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...Several multi-objective optimization algorithms are based on PSO such as Multi-Objective PSO (MOPSO) [24], and Non-Dominated Sorting PSO (NSPSO) [25]....
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27 Sep 1998
TL;DR: In this paper an extensive, quantitative comparison is presented, applying four multiobjective evolutionary algorithms to an extended 0/1 knapsack problem.
Abstract: Since 1985 various evolutionary approaches to multiobjective optimization have been developed, capable of searching for multiple solutions concurrently in a single run. But the few comparative studies of different methods available to date are mostly qualitative and restricted to two approaches. In this paper an extensive, quantitative comparison is presented, applying four multiobjective evolutionary algorithms to an extended 0/1 knapsack problem.
2,401 citations
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...HyperVolume indicator [18], [35] is a measure used in evolutionary multi-objective optimization, which reflects the volume enclosed by a solution set and a reference point....
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