Concept Of Opposition-based Learning Research Articles

Boosting salp swarm algorithm by opposition-based learning concept and sine cosine algorithm for engineering design problems

Boosting salp swarm algorithm by opposition-based learning concept and sine cosine algorithm for engineering design problems

Particle swarm optimisation with opposition learning-based strategy: an efficient optimisation algorithm for day-ahead scheduling and reconfiguration in active distribution systems

In operation of active electric distribution networks, optimal configuration and schedule of distributed generation and reactive power resources are determined. This represents a formidable multi-modal constrained optimisation problem with discrete decision variables. Metaheuristics are the most common approaches for solving this problem. However, due to its multi-modal nature, metaheuristics commonly converge prematurely into local optima and cannot find near-global solutions. In this research, a new particle swarm optimisation (PSO) variant is put forward for finding optimal configuration and schedule of distributed generation and reactive power resources in distribution systems including both dispatchable and renewable distributed energy resources. In the proposed PSO variant, opposition-based learning concept is incorporated into PSO which reduces premature convergence probability through enhancement of swarm leaders. The results of the proposed opposition-based PSO in IEEE 69 bus system indicate its outperformance over conventional PSO, time-varying acceleration coefficient PSO, fractal optimisation algorithm and evolutionary programming.

Enhancing evacuation response to extreme weather disasters using public transportation systems: a novel simheuristic approach

Abstract In recent years, there have been an increasing number of extreme weather events that have had major impacts on the built environment and particularly on people living in urban areas. As the frequency and intensity of such events are predicted to increase in the future, innovative response strategies to cope with potential emergency conditions, particularly evacuation planning and management, are becoming more important. Although mass transit evacuation of populations at risk is recognized to play a potentially important role in reducing injury and mortality rates, there is relatively little research in this area. In answering the need for more research in this increasingly important and relatively new field of research, this study proposes a hybrid simulation–optimization approach to maximize the number of evacuees moved from disaster-affected zones to safe locations. In order to improve the efficiency of the proposed optimization approach, a novel multipopulation differential evolution approach based on an opposition-based learning concept is developed. The results indicate that even for large populations the proposed approach can produce high-quality options for decision makers in reasonable computational times. The proposed approach enables emergency decision makers to apply the procedure in practice to find the best strategies for evacuation, even when the time for decision making is severely limited.

An opposition-based memetic algorithm for the maximum quasi-clique problem

Given a simple undirected graph G=(V,E) and a constant γ, the γ-quasi-clique is defined as a subset of vertices that induces a subgraph with the edge density of at least γ. The maximum γ-quasi-clique problem (MQCP) is to find a γ-quasi-clique of the maximum cardinality in G. This problem has many practical applications, especially in social network analysis. We present an opposition-based memetic algorithm (OBMA) for MQCP, which relies on a backbone-based crossover operator to generate new offspring solutions and on a constrained neighborhood tabu search for local improvement. OBMA further integrates the concept of opposition-based learning (OBL) to enhance the search ability of the classic memetic algorithm. Computational results on a large set of both dense and sparse graphs show that the proposed heuristic competes very favorably with the current state-of-the-art algorithms from the MQCP literature. In particular, it is able to find improved best-known solutions for 47 out of the 100 dense graphs, while reaching the best-known solution for all but few of the remaining instances. Several essential components of the proposed approach are investigated to understand their impacts to the algorithm’s performance.

一种基于自适应搜索策略的改进萤火虫算法

萤火虫算法(FA)是一种基于群智能的优化技术，它在很多优化问题上表现出较好的性能。然而，它求解复杂优化问题时存在一些问题，如收敛速度慢，精度低。针对这些问题，本文提出了一种新的萤火虫算法(取名AFA)，该方法使用了三种混合策略，以获得好的优化性能。它首先使用一种自适应的参数方法来动态改变步长参数，然后应用一种改进的搜索策略来消除吸引力，于是，AFA不再包含光吸收系数和初始吸引力这2个参数；再使用反向学习来提高解的精度。仿真结果表明，本文提出的AFA算法优化结果优于MFA及PAFA算法。 Firefly algorithm (FA) is a recently proposed optimisation technique, based on swarm intelligence, which has shown good optimisation performance. However, FA suffers from slow convergence and low accuracy of solutions. To improve this case, this paper presents a new firefly algorithm (AFA) by using three hybrid strategies to obtain a good optimisation performance. First, an adaptive parameter method is used to dynamically changing the step factor. Second, AFA uses a modified search strategy and eliminates the concept of attractiveness. So, HFA does not include two parameters, ab-sorption coefficient and initial attractiveness. Third, a concept of opposition-based learning is used for improving the accuracy of the global best solution. Experiments on some benchmark problems show that AFA is superior to mimetic FA (MFA) and probabilistic attraction-based FA (PAFA).

Development and applications of an intelligent crow search algorithm based on opposition based learning

Metaheuristics are proven beneficial tools for solving complex, hard optimization problems. Recently, a plethora of work has been reported on bio inspired optimization algorithms. These algorithms are mimicry of behavior of animals, plants and processes into mathematical paradigms. With these developments, a new entrant in this group is Crow Search Algorithm (CSA). CSA is based on the strategic behavior of crows while searching food, thievery and chasing behavior. This algorithm sometimes suffers with local minima stagnation and unbalance exploration and exploitation phases. To overcome this problem, a cosine function is proposed first, to accelerate the exploration and retard the exploitation process with due course of the iterative process. Secondly the opposition based learning concept is incorporated for enhancing the exploration virtue of CSA. The evolved variant with the inculcation of these two concepts is named as Intelligent Crow Search Algorithm (ICSA). The algorithm is benchmarked on two benchmark function sets, one is the set of 23 standard test functions and another is set of latest benchmark function CEC-2017. Further, the applicability of this variant is tested over structural design problem, frequency wave synthesis problem and Model Order Reduction (MOR). Results reveal that ICSA exhibits competitive performance on benchmarks and real applications when compared with some contemporary optimizers.

A novel firefly algorithm for parameter estimation

Firefly algorithm (FA) is a recently proposed optimisation technique, which has shown good optimisation performance. However, FA suffers from slow convergence and low accuracy of solutions. To improve this case, this paper presents a novel FA (NFA) by combining two strategies. First, a local search operator is constructed for better fireflies in the population. Second, a concept of opposition-based learning is used for improving the accuracy of the global best solution. The experiment consists of two parts: (1) seven classical benchmark functions are used to verify the optimisation ability of NFA; and (2) NFA is used for parameter estimation of frequency modulated (FM) sound synthesis. Simulation results show the NFA approach can achieve promising performance.

A novel firefly algorithm for parameter estimation

Firefly algorithm (FA) is a recently proposed optimisation technique, which has shown good optimisation performance. However, FA suffers from slow convergence and low accuracy of solutions. To improve this case, this paper presents a novel FA (NFA) by combining two strategies. First, a local search operator is constructed for better fireflies in the population. Second, a concept of opposition-based learning is used for improving the accuracy of the global best solution. The experiment consists of two parts: (1) seven classical benchmark functions are used to verify the optimisation ability of NFA; and (2) NFA is used for parameter estimation of frequency modulated (FM) sound synthesis. Simulation results show the NFA approach can achieve promising performance.

Opposition-Based Memetic Search for the Maximum Diversity Problem

As a usual model for a variety of practical applications, the maximum diversity problem (MDP) is computational challenging. In this paper, we present an opposition-based memetic algorithm (OBMA) for solving MDP, which integrates the concept of opposition-based learning (OBL) into the well-known memetic search framework. OBMA explores both candidate solutions and their opposite solutions during its initialization and evolution processes. Combined with a powerful local optimization procedure and a rank-based quality-and-distance pool updating strategy, OBMA establishes a suitable balance between exploration and exploitation of its search process. Computational results on 80 popular MDP benchmark instances show that the proposed algorithm matches the best-known solutions for most of instances, and finds improved best solutions (new lower bounds) for 22 instances. We provide experimental evidences to highlight the beneficial effect of OBL for solving MDP.

Opposition based learning ingrained shuffled frog-leaping algorithm

Shuffled frog-leaping algorithm (SFLA) is a kind of memetic algorithm. Randomicity and determinacy, the two keywords of SFLA ensures flexibility, robustness and exchange of information effectively in SFLA. In the basic structure of SFLA, the frogs are divided into memeplexes based on their fitness values where they forage for food. In this study the opposition based learning concept is embedded into the memeplexes before the frog initiates foraging. The proposal is investigated, analyzed and compared with latest variants of SFLA on benchmark functions (unimodal and multimodal) along with a real life problem. The result analysis shows that the proposed variant performs consistently well for different types of problems considered in this study.

Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints

Optimal reactive power dispatch (ORPD) is well known as a complex mixed integer nonlinear optimization problem where many constraints are required to handle. In the last decades, many artificial intelligence-based optimization methods have been used to solve ORPD problem. But, these optimization methods lack an effective means to handle constraints on state variables. Thus, in this paper, the novel and feasible conditional selection strategies (CSS) are devised to handle constraints efficiently in the proposed improved gravitational search algorithm (GSA-CSS). In addition, considering the weakness of GSA itself, the improved GSA-CSS (IGSA-CSS) is presented which employs the memory property of particle swarm optimization (PSO) to enhance global searching ability and utilizes the concept of opposition-based learning (OBL) for optimizing initial population. The presented GSA-CSS and IGSA-CSS methods are applied to ORPD problem on IEEE14-bus, IEEE30-bus and IEEE57-bus test systems for minimization of power transmission losses (Ploss) and voltage deviation (Vd), respectively. The comparisons of simulation results reveal that IGSA-CSS provides better results and the improvements of algorithm in this work are feasible and effective.

Opposition learning based phases in artificial bee colony

Artificial bee colony (ABC) is a recently introduced swarm intelligence algorithm (SIA). Initially only unconstrained problems were handled by ABC, which was later modified by embedding one more parameter called modified rate to handle constrained problems. Since then, ABC and its variants have shown a remarkable success in the domain of swarm intelligence optimization algorithms. The exploration capability of ABC is comparatively better than exploitation which sometimes limits the convergence rate of ABC while handling multimodal optimization problems. In this study the foraging process of two phases has been enhanced by embedding opposition based learning concept. This modification is introduced to enhance the acceleration and exploitation capability of ABC. The variant is named as O-ABC (Opposition based ABC). The efficiency of O-ABC is initially evaluated on 12 benchmark functions consulted from literature. Later O-ABC is applied for intrusion detection. The simulated comparative results have shown the competitiveness of the proposal.

Hybridized Gravitational Search Algorithm for Short-Term Hydrothermal Scheduling

ABSTRACTThis paper presents a new approach for solving the short-term hydrothermal scheduling (STHTS) problem using a disruption operator in an oppositional gravitational search algorithm. The nonlinear and non-convex nature of the STHTS problem coupled with the cascading nature of reservoirs, water transport delays and scheduling time linkages makes the solution of this optimization problem quite difficult using the conventional optimization methods. Here, an opposition-based learning concept is introduced in a gravitational search algorithm to improve the quality of the current population towards global optimal solutions and a disruption operator is integrated to accelerate the convergence of solutions. This method is evaluated on two test systems consisting of four hydro and an equivalent thermal plant and four hydro and three thermal plants. The detailed statistical results prove that the proposed approach performs better in terms of production cost and smooth convergence characteristics when compared with other recently reported methods in the literature.

Enhancing firefly algorithm using generalized opposition-based learning

Firefly algorithm has been shown to yield good performance for solving various optimization problems. However, under some conditions, FA may converge prematurely and thus may be trapped in local optima due to loss of population diversity. To overcome this defect, inspired by the concept of opposition-based learning, a strategy to increase the performance of firefly algorithm is proposed. The idea is to replace the worst firefly with a new constructed firefly. This new constructed firefly is created by taken some elements from the opposition number of the worst firefly or the position of the brightest firefly. After this operation, the worst firefly is forced to escape from the normal path and can help it to escape from local optima. Experiments on 16 standard benchmark functions show that our method can improve accuracy of the basic firefly algorithm.

An opposition-based algorithm for function optimization

The concept of opposition-based learning (OBL) was first introduced as a scheme for machine intelligence. In a very short period of time, some other variants of opposite numbers were proposed and opposition was applied to various research areas. In metaheuristic optimization algorithms, the main idea behind applying opposite numbers is the simultaneous consideration of a candidate solution and its corresponding opposite candidate in order to achieve a better approximation for the current solution. This paper proposes an opposition-based metaheuristic optimization algorithm (OBA) and a new and efficient opposition named comprehensive opposition (CO) as its main operator. In this paper it is mathematically proven that CO not only increases the chance of achieving better approximations for the solution but also guarantees the global convergence of OBA. The efficiency of the proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various function optimizations.

Opposition-based firefly algorithm for earth slope stability evaluation

This paper introduces a new approach of firefly algorithm based on opposition-based learning (OBFA) to enhance the global search ability of the original algorithm. The new algorithm employs opposition based learning concept to generate initial population and also updating agents’ positions. The proposed OBFA is applied for minimization of the factor of safety and search for critical failure surface in slope stability analysis. The numerical experiments demonstrate the effectiveness and robustness of the new algorithm.

Balanced Cartesian Genetic Programming via migration and opposition-based learning: application to symbolic regression

The exploration---exploitation trade-off is an important aspect of evolutionary algorithms which determines the efficiency and accuracy of these algorithms. Cartesian Genetic Programming (CGP) is a generalization of the graph based genetic programming. It is implemented with mutation only and does not have any possibility to share information among solutions. The main goal of this paper is to present an effective method for balancing the exploration and exploitation of CGP referred to as Balanced Cartesian Genetic Programming (BCGP) by incorporating distinctive features from biogeography-based optimization (BBO) and opposition-based learning. To achieve this goal, we apply BBO's migration operator without considering any modifications in the representation of CGP. This operator has good exploitation ability and can be used to share information among individuals in CGP. In addition, in order to improve the exploration ability of CGP, a new mutation operator is integrated into CGP inspired from the concept of opposition-based learning. Experiments have been conducted on symbolic regression. The experimental results show that the proposed BCGP method outperforms the traditional CGP in terms of accuracy and the convergence speed.

Efficient gravitational search algorithm for optimum design of retaining walls

【In this paper, a new version of gravitational search algorithm based on opposition-based learning (OBGSA) is introduced and applied for optimum design of reinforced concrete retaining walls. The new algorithm employs the opposition-based learning concept to generate initial population and updating agents' position during the optimization process. This algorithm is applied to minimize three objective functions include weight, cost and $CO_2$ emissions of retaining structure subjected to geotechnical and structural requirements. The optimization problem involves five geometric variables and three variables for reinforcement setups. The performance comparison of the new OBGSA and classical GSA algorithms on a suite of five well-known benchmark functions illustrate a faster convergence speed and better search ability of OBGSA for numerical optimization. In addition, the reliability and efficiency of the proposed algorithm for optimization of retaining structures are investigated by considering two design examples of retaining walls. The numerical experiments demonstrate that the new algorithm has high viability, accuracy and stability and significantly outperforms the original algorithm and some other methods in the literature.】

Quantum Behaved Particle Swarm Optimization with Neighborhood Search for Numerical Optimization

Quantum-behaved particle swarm optimization (QPSO) algorithm is a new PSO variant, which outperforms the original PSO in search ability but has fewer control parameters. However, QPSO as well as PSO still suffers from premature convergence in solving complex optimization problems. The main reason is that new particles in QPSO are generated around the weighted attractors of previous best particles and the global best particle. This may result in attracting too fast. To tackle this problem, this paper proposes a new QPSO algorithm called NQPSO, in which one local and one global neighborhood search strategies are utilized to balance exploitation and exploration. Moreover, a concept of opposition-based learning (OBL) is employed for population initialization. Experimental studies are conducted on a set of well-known benchmark functions including multimodal and rotated problems. Computational results show that our approach outperforms some similar QPSO algorithms and five other state-of-the-art PSO variants.

Opposition-Based Artificial Bee Colony with Dynamic Cauchy Mutation for Function Optimization

This paper presents a new Artificial Bee Colony (ABC) optimization algorithm to solve function optimization problems. The proposed approach is called OCABC, which introduces opposition-based learning concept and dynamic Cauchy mutation into the standard ABC algorithm. To verify the performance of OCABC, eight well-known benchmark function optimization problems are used in the experiments. Experimental results show that our approach outperforms the original ABC, Particle Swarm Optimization (PSO) and opposition-based PSO for the majority of test functions.