Proximate Optimality Principle Research Articles

A ranking-based fuzzy adaptive hybrid crow search algorithm for combined heat and power economic dispatch

This paper attempts to obtain optimal generation scheduling for Combined Heat and Power Economic Dispatch (CHPED) problems and seeking a possible solution for the global optimization of test systems. As such, a Fuzzy adaptive Ranking- based Crow Search Algorithm (FRCSA) is amalgamated with modified Artificial Bee Colony (ABC). The proposed algorithm (FRCSA-ABC) has been integrated with three mechanisms in order to achieve competitive results effectively. The first mechanism uses the Fuzzy logic Inference System (FIS) to tune dynamically the parameters of flight length (fl) and awareness probability (CAP) of each crow. The second mechanism emerges from a natural phenomenon known as proximate optimality principle (POP) which reveals that better individuals will often have beneficial information and more probabilities to select and guide other individuals. This observation leads to the feasibility-based Ranking Crow Search Algorithm (RCSA) which generates a new food source from the chosen higher rankings of parent food sources. The third mechanism integrates the two modified global search phases of ABC with a local search phase of FRCSA to ensure promising performance. During the evaluation process, this mechanism investigates the aging level of the individual’s best solution (pbest) in order to choose an appropriate search phase in FRCSA and two modified phases of ABC. The performance of the algorithm is tested on four CHPED test systems, 23 well-known benchmark functions, and test suit of CEC2017. The results obtained are compared with its variants as well as with existing different approaches to validate its optimal search efficiency. Further, non-parametric statistical tests such as pair-wise and multiple comparisons tests are adapted to establish the supremacy of the FRCSA-ABC. The seminal aspect of this intended algorithm is that it can achieve cost-effectiveness conforming to meticulous global convergence.

ERG-DE: An elites regeneration framework for differential evolution

Differential evolution (DE) is one of the most popular paradigms of evolutionary algorithms. Numerous variants of basic DE have been developed in the past two decades after it was first proposed. However, very few works focused on re-exploring the neighborhood area of the elite solutions, which is definitely a promising area according to the proximate optimality principle. Here, a simple yet efficient elites regeneration (ERG) framework was designed to fill this gap. The elite population in this framework is defined as a group of individuals with better fitness values and they are regenerated after the selection procedure in DE. Specifically, a new individual is produced from the search space around each elite individual (i.e. the parent individual) by sampling Gaussian or Cauchy probability models and replaces the parent if it has better fitness value. The implementation of this procedure only introduces two parameters that need to be tuned, i.e. the standard deviation for Gaussian distribution and the scale parameter for the Cauchy distribution. In the proposed framework, the elite individuals serve as the mean or location parameters of the probability models and the standard deviation and scale parameters are tuned by experiments as a small constant value. Thus, offspring individuals are generated in areas close to their corresponding elite parents. The framework allows thorough exploitation of search neighborhoods around elite individuals and ultimately helps the elite individuals escaping from local optima. Experiments results on CEC2014 benchmark revealed that ERG framework significantly increased the optimization capacity for four original DE algorithms, four classical DE variants, and two state-of-the-art DE variants. In addition, it also demonstrated competitive performance when compared with another DE framework.

問題構造の概形の推定機構を有する多点探索型組合せ最適化手法

Based on the Proximate Optimality Principle (POP) and a big valley structure in combinatorial optimization problems, an estimation mechanism for quantitatively estimating structural characteristics (landscape) of combinatorial optimization problems is developed in this paper. Using the results of a numerical evaluation of landscape for several types of combinatorial optimization problems including a traveling salesman problem, a 0-1 knapsack problem, a flow-shop scheduling problem and a quadratic assignment problem, a new multi-point combinatorial optimization method having the landscape estimation mechanism is also proposed. The proposed combinatorial optimization method uses the estimated landscape information of a given combinatorial optimization problem to control diversification and intensification during a search. The search capabilities of the proposed combinatorial optimization method are examined based on the results of numerical experiments using typical benchmark problems.

Combinatorial Optimization Method Based on Quantitative Evaluation of Proximate Optimality Principle

Combinatorial Optimization Method Based on Quantitative Evaluation of Proximate Optimality Principle

Combinatorial Optimization Method Based on Proximate Optimality Principle Using Neighborhood Search

This paper proposes a new neighborhood search method for solving combinatorial optimization problems based on Proximate Optimality Principle (POP). The proposed combinatorial optimization method has higher optimality and lower computational complexity than conventional neighborhood search methods. The proposed method is applied to several typical combinatorial optimization problems in order to verify the performance of the proposed method.

Proximate Optimality Principle Based Multi-point Tabu Search

This paper proposes a new Tabu Search with multi search points. In the proposed method, Proximate Optimality Principle (POP) is taken into consideration in order to improve its search efficiency. The proposed method is applied to some traveling salesman problems to verify the performance of the proposed algorithm.

115 近接最適性原理に基づく相互作用を用いた組合せ最適化手法

115 近接最適性原理に基づく相互作用を用いた組合せ最適化手法

Multipoint Tabu Search based on proximate optimality principle—application of parts concept

AbstractThis paper proposes an algorithm–MultiPoint Tabu Search based on proximate optimality principle (POP), which has several advantages for solving combinatorial optimization problems. POP holds well for numerous engineering problems, which stipulates that ‘good solutions possess some similar structure.’ The proposed algorithm is applied to several typical combinatorial optimization problems to verify the performance of the proposed algorithm. The simulation results indicate that the proposed method has higher optimality than the conventional Tabu Search with a single search point. Copyright © 2007 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A basic study of adaptive particle swarm optimization

AbstractThis paper points out that meta‐heuristics should have not only robustness and adaptability to problems with different structure but also adjustability of parameters included in their algorithms. Particle swarm optimization (PSO), whose concept began as a simulation of a simplified social milieu, is known as one of the most powerful optimization methods for solving nonconvex continuous optimization problems. Then, in order to improve adjustability, a new parameter is introduced into PSO on the basis of the proximate optimality principle (POP). In this paper, we propose adaptive PSO and the effectiveness and the feasibility of the proposed approach are demonstrated on simulations using some typical nonconvex optimization problems. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 151(3): 41–49, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20077

POP analysis and optimization of a maintenance scheduling problem in electric power systems

This paper presents the POP (Proximate Optimality Principle) analysis and the optimization of a generator maintenance scheduling in electric power systems. The generator maintenance scheduling problem is to determine an optimal maintenance period of the respective generators with various constrains, and can be formulated as a combinatorial optimization problem. While POP analysis of the generator maintenance scheduling problem is carried out and using some different types of model systems, improvement of Tabu Search is achieved by the reduction of search space. The advantages of the proposed approach are demonstrated on typical model systems.

Proximate Optimality Principleに基づくTabu Search

This paper presents a new method for combinatorial optimization problems. Most of the actual problems that have discrete structure can be formulated as combinatorial optimization problems. It is experientially known that Proximate Optimality Principle (POP) holds in most of the actual combinatorial optimization problems. The concept of Proximate Optimality Principle says that good solutions of most real combinatorial optimization problems have the structural similarity in parts of solution. In this paper we propose a new optimization method based on Tabu Search. In the proposed algorithm, POP is taken into consideration. The proposed algorithm is applied to some knapsack problems, which are typical combinatorial optimization problems in order to verify the performance of the proposed algorithm.

Proximate Optimality Principle Based Tabu Search

Meta-heuristics is a new paradigm that aims to obtain an approximate solution within a feasible computation time. In the meta-heuristics, Tabu Search is one of the most effective algorithms for solving combinatorial optimization problems. While the intensification of Tabu Search is powerful, the diversification of Tabu Search is not powerful. This paper proposes an algorithm - Multi Criteria Tabu Search coordinating the intensification and the diversification based on a Proximate Optimality Principle (POP) - which has several advantages for solving combinatorial optimization problems. The proposed algorithm is applied to some traveling salesman problems which are typical combinatorial optimization problems in order to verify the performance of the proposed algorithm.

適応型Particle Swarm Optimizationに関する基礎的検討

This paper points out that meta-heuristics should have not only robustness and adaptability to problems with different structure but also adjustability of parameters included in their algorithms. Particle Swarm Optimization (PSO), whose concept began as a simulation of a simplified social milieu, is known as one of the most powerful optimization methods for solving nonconvex continuous optimization problems. Then, in order to improve adjustability, a new parameter is introduced into particle swarm optimization on the basis of the Proximate Optimality Principle (POP). In this paper, we propose adaptive Particle Swarm Optimization and the effectiveness and the feasibility of the proposed approach are demonstrated on simulations using some typical nonconvex optimization problems.

207 組合せ最適化問題における Proximate Optimality Principle を考慮した Tabu Search の探索効率改善

207 組合せ最適化問題における Proximate Optimality Principle を考慮した Tabu Search の探索効率改善

203 ロバスト適応型 Particle Swarm Optimization

This paper points out that optimization methods should have robustness and adaptability to problems with different structure and adjustability of parameters provides adaptability. Particle swarm optimization, whose concept began as a simulation of a simplified social milieu, is known as one of the most powerful optimization methods for solving nonconvex continuous optimization problems. Then, in order to improve adjustability, several new parameters are introduced to particle swarm optimization on the basis of the Proximate Optimality Principle (POP). In this paper, we propose Adaptive Particle Swarm Optimization. And the effectiveness and feasibility of the proposed approach are demonstrated on simulations using typical nonconvex optimization problems.