Traditional multiobjectiveoptimization problems (MOPs) are insufficiently equipped for scenarios involving multiple decision makers (DMs), which are prevalent in many practical applications. These scenarios are categ...
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Traditional multiobjectiveoptimization problems (MOPs) are insufficiently equipped for scenarios involving multiple decision makers (DMs), which are prevalent in many practical applications. These scenarios are categorized as multiparty multiobjective optimization problems (MPMOPs). For MPMOPs, the goal is to find a solution set that is as close to the Pareto front of each DM as much as possible. This poses challenges for evolutionary algorithms in terms of searching and selecting. To better solve MPMOPs, this paper proposes a novel approach called the multiparty immune algorithm (MPIA). The MPIA incorporates an inter-party guided crossover strategy based on the individual's non-dominated sorting ranks from different DM perspectives and an adaptive activation strategy based on the proposed multiparty cover metric (MCM). These strategies enable MPIA to activate suitable individuals for the next operations, maintain population diversity from different DM perspectives, and enhance the algorithm's search capability. To evaluate the performance of MPIA, we compare it with ordinary multiobjective evolutionary algorithms (MOEAs) and state-of-the-art multiparty multiobjective optimization evolutionary algorithms (MPMOEAs) by solving synthetic multipartymultiobjective problems and real-world biparty multiobjective unmanned aerial vehicle path planning (BPUAV-PP) problems involving multiple DMs. Experimental results demonstrate that MPIA outperforms other algorithms.
As a special class of multiobjectiveoptimization problems (MOPs), multiparty multiobjective optimization problems (MPMOPs) widely exist in real-world applications. In MPMOPs, there are multiple decision makers (DMs) ...
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ISBN:
(数字)9781665467087
ISBN:
(纸本)9781665467087
As a special class of multiobjectiveoptimization problems (MOPs), multiparty multiobjective optimization problems (MPMOPs) widely exist in real-world applications. In MPMOPs, there are multiple decision makers (DMs) concerning multiple different conflicting objectives. The goal of solving MPMOPs is to catch the best solutions satisfying all DMs as far as possible. To our best knowledge, there is little attention on solving MPMOPs, and only two optimization algorithms, i.e., OptMPNDS and OptMPNDS2, are proposed. These two algorithms are both based on non-dominated sorting genetic algorithm II (NSGA-II). However, there is no algorithm proposed from the decomposition perspective to solve MPMOPs. multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a popular multiobjective evolutionary optimization algorithm for MOPs. In this paper, we embed the party-by-party strategy into MOEA/D and propose the novel optimization algorithm MOEA/D-MP to solve MPMOPs. The experimental results on the benchmarks have demonstrated the effectiveness of MOEA/D-MP.
Some real-world optimization problems involve multiple decision makers holding different positions, each of whom has multiple conflicting objectives. These problems are defined as multipartymultiobjective optimizatio...
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ISBN:
(数字)9781728169293
ISBN:
(纸本)9781728169293
Some real-world optimization problems involve multiple decision makers holding different positions, each of whom has multiple conflicting objectives. These problems are defined as multiparty multiobjective optimization problems (MPMOPs). Although evolutionary multiobjectiveoptimization has been widely studied for many years, little attention has been paid to multiparty multiobjective optimization in the field of evolutionary computation. In this paper, a class of MPMOPs, that is, MPMOPs having common Pareto optimal solutions, is addressed. A benchmark for MPMOPs, obtained by modifying an existing dynamic multiobjectiveoptimization benchmark, is provided, and a multipartymultiobjective evolutionary algorithm to find the common Pareto optimal set is proposed. The results of experiments conducted using the benchmark show that the proposed multipartymultiobjective evolutionary algorithm is effective.
Many real-world optimization problems require optimizing multiple conflicting objectives simultaneously, and such problems are called multiobjectiveoptimization problems (MOPs). As a variant of the classical knapsack...
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ISBN:
(纸本)9781665487689
Many real-world optimization problems require optimizing multiple conflicting objectives simultaneously, and such problems are called multiobjectiveoptimization problems (MOPs). As a variant of the classical knapsack problems, multiobjective knapsack problems (MOKPs), exist widely in the realworld applications, e.g., cargo loading, project and investment selection. There is a special class of MOKPs called multipartymultiobjective knapsack problems (MPMOKPs), which involve multiple decision makers (DMs) and each DM only cares about some of all the objectives. To the best of our knowledge, little work has been conducted to address MPMOKPs. In this paper, a set of benchmarks which have common Pareto optimal solutions for MPMOKPs is proposed. Besides, we design a SPEA2-based algorithm, called SPEA2-MP to solve MPMOKPs, which aims at finding the common Pareto optimal solutions to satisfy multiple decision makers as far as possible. Experimental results on the benchmarks have demonstrated the effectiveness of the proposed algorithm.
The multiobjective optimal power flow (MOOPF) problem consists of adjusting the generator power and the voltage state of each node within the feasible range in the process of power transmission and, finally of achievi...
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The multiobjective optimal power flow (MOOPF) problem consists of adjusting the generator power and the voltage state of each node within the feasible range in the process of power transmission and, finally of achieving the objectives of optimizing the cost, loss and stability, etc. In the MOOPF, two key decision makers are usually involved, which are the power generation sector and the transmission sector. Thus, it is more suitable to model an MOOPF as a biparty multiobjective optimal power flow (BPMOOPF) problem. However, so far, there is no work on treating and solving the MOOPF problem from the perspective of biparty multiobjectiveoptimization. In this paper, we propose the definition of the BPMOOPF problem as well as a novel evolutionary biparty multiobjectiveoptimization algorithm for solving the BPMOOPF problem, which we call BPMOOPF-EA. Our experimental results show that, compared two state-of-the-art algorithms (C-MOEA/D and A-NSGA-III), our proposed BPMOOPF-EA has a better performance when solving the BPMOOPF problem. (c) 2023 Elsevier B.V. All rights reserved.
multiparty multiobjective optimization problems (MPMOPs) have been proposed to represent situations in which involves multiple decision makers, each decision maker concerns on a multiobjectiveoptimization problem (MO...
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multiparty multiobjective optimization problems (MPMOPs) have been proposed to represent situations in which involves multiple decision makers, each decision maker concerns on a multiobjectiveoptimization problem (MOP) and their MOPs are different. To study multipartymultiobjective evolutionary algorithms in depth, this paper constructs a series of MPMOPs based on distance minimization problems (DMPs). These MPMOPs, called MPDMPs, can easily represent the solutions in the decision space. Thus, the behaviors of evolutionary algorithms performing on MPDMPs can be conveniently studied including the movement of the solutions and the distribution of the final solutions. To address MPDMPs, the new proposed algorithm OptMPNDS3 uses a multiparty initialization method to initialize the population and the JADE2 operator to generate the offspring. OptMPNDS3 is compared with OptAll, OptMPNDS and OptMPNDS2 on the problem suite. The results show that the performance of OptMPNDS3 is strong and comparable to that of other algorithms.
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