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Surrogate-assisted evolutionary algorithms have received a surge of attentions for their promising ability of solving expensive optimization problems. Existing surrogate-assisted evolutionary algorithms usually adopt the regression models and the binary classification models to guide the evolution of the population for solving the multiobjective optimization problems. However, the regression models will make the algorithm to be increasingly computation-expensive as the number of objectives increases, while the use of the binary classification models might suffer from the poor diversity since the diversified information of solutions cannot be reflected in these classification models. For this issue, this paper proposes a surrogate-assisted many-objective evolutionary algorithm using the cooperation of the multi-classification and regression models to improve the search quality while reducing the computational cost. Our approach includes two parts: At the model training stage, a multi-classification model is constructed to divide the whole population into several classes for ensuring diversity, a distance regression model and an angle regression model are used to select solutions with better convergence and diversity in each class; At the evolution stage, a coevolutionary framework is used to guide the evolution according to a new selection criterion. Experimental results verify the effectiveness of the proposed algorithm on a set of expensive test problems with up to 10 objectives.

The Multiobjective Evolutionary Optimization algorithms (MOEAs) have attracted lots of attention and have been used for resolving engineering problems, such as production scheduling, logistics planning, and intelligent storage. This paper focuses on the modification of the Stable Matching-based Selection (STM) in Evolutionary Multiobjective Optimization (MOEA/D-STM). Generally speaking, the mechanism would strike an equilibrium between the preferences among scalar problems and solutions. Nevertheless, the initial stable matching model may have a high weakness of mismatching with a certain solution. To address the drawback, this paper proposes the Decomposition Based MOEA with Unique Subregions and Stable Matching (MOEA/D-RM). MOEA/D-RM decomposes the multiobjective optimization problem into a number of various subregions for remedying the loss of diversity and introduces the incomplete preference lists into the STM model for repairing the loss of convergence. The effectiveness and competitiveness of MOEA/D-RM are corroborated and compared with several evolutionary multiobjective optimization algorithms.

For solving the problems of low accuracy, low convergence speed and accidental premature of canonical whale optimization algorithm (WOA), a modified multi-population WOA based on orthogonal learning (MOL-WOA) is proposed. Firstly, the multi-population strategy is adopted. The diversity of the sub-populations can be improved by the generalized opposition-based learning (GOBL) strategy. Secondly, the nonlinear convergence factor and adaptive weight strategy are utilized to take account of the global search and local development. Finally, the orthogonal learning method is adopted to realize the communication among the sub-populations. At the same time, a generation jump operation is brought in for avoiding premature. Simulation experiments with 20 benchmark test functions, contrasted with the other two modified algorithms, MOL-WOA owns better performance on accuracy, convergence speed and ability to miss the local optimal.

Radio frequency identification (RFID) networks planning (RNP) is a challenging task on how to deploy RFID readers under certain constraints. Existing RNP models are usually derived from the flat and centralized-processing framework identified by vertical integration within a set of objectives which couple different types of control variables. This paper proposes a two-level RNP model based on the hierarchical decoupling principle to reduce computational complexity, in which the cost-efficient planning at the top levels is modeled with a set of discrete control variables (i.e., switch states of readers), and the quality of service objectives at the bottom level are modeled with a set of continuous control variables (i.e., physical coordinate and radiate power). The model of the objectives at the two levels is essentially a multiobjective problem. In order to optimize this model, this paper proposes a specific multiobjective artificial bee colony optimizer called H-MOABC, which is based on performance indicators with reinforcement learning and orthogonal Latin squares approach. The proposed algorithm proves to be competitive in dealing with two-objective and three-objective optimization problems in comparison with state-of-the-art algorithms. In the experiments, H-MOABC is employed to solve the two scalable real-world RNP instances in the hierarchical decoupling manner. Computational results shows that the proposed H-MOABC is very effective and efficient in RFID networks optimization.

Portfolio management is an important technology for reasonable investment, fund management, optimal asset allocation, and effective investment. Portfolio optimization problem (POP) has been recognized as an NP-hard problem involving numerous objectives as well as constraints. Applications of evolutionary algorithms and swarm intelligence optimizers for resolving multi-objective POP (MOPOP) have attracted considerable attention of researchers, yet their solutions usually convert MOPOP to POP by means of weighted coefficient method. In this paper, a multi-swarm multi-objective optimizer based on p-optimality criteria called p-MSMOEAs is proposed that tries to find all the Pareto optimal solutions by optimizing all objectives at the same time, rather than through the above transforming method. The proposed p-MSMOEAs extended original multiple objective evolutionary algorithms (MOEAs) to cooperative mode through combining p-optimality criteria and multi-swarm strategy. Comparative experiments of p-MSMOEAs and several MOEAs have been performed on six mathematical benchmark functions and two portfolio instances. Simulation results indicate that p-MSMOEAs are superior for portfolio optimization problem to MOEAs when it comes to optimization accuracy as well as computation robustness.

Resource sharing in a distributed environment can improve the utilization of resources. To promote the benign development of resource sharing behavior, a basic evaluation model of resource sharing is proposed, and an evaluation index system of distributed server resource sharing is constructed based on the model in this paper. The judgment matrices are constructed according to the analytic hierarchy process (AHP), and the simulated annealing algorithm (SAA) and the improved simulated annealing algorithm (ISAA) are respectively used to improve the consistency of each judgment matrix. The results show that the average consistency deviation of the judgment matrices optimized by ISAA is reduced 0.0421 and 0.0106 compared to EM and SAA respectively, and the convergence speed is about 77.8% higher than SAA. The standard deviations of the weight differences before and after optimization using ISAA and SAA are 0.0074 and 0.0259, respectively, so the weight fluctuations after ISAA optimization are relatively smaller. The weight distribution corresponding to each index is obtained when the consistencies of the judgment matrices are close to the optimal state, which provides a necessary technical foundation for the evaluation of distributed server resource sharing.

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is widely used to dispose multiobjective optimization problems (MOPs). The performance of MOEA/D is not ideal when deals with MOPs which are with degenerated curve. Aiming to this weakness, an adaptive weight adjustment strategy named BPO is proposed in this paper. In order to solve this problem, BPO strategy is based on the number of reference points referencing the archive. The results of tests show that MOEA/D with BPO strategy can obtain ideal performance. MOEA/D with BPO strategy can improves the weakness of MOEA/D effectively on MOPs which are with degenerated curve.

This paper proposes a novel multi-objective root system growth optimizer (MORSGO) for the copper strip burdening optimization. The MORSGO aims to handle multi-objective problems with satisfactory convergence and diversity via implementing adaptive root growth operators with a pool of multi-objective search rules and strategies. Specifically, the single-objective root growth operators including branching, regrowing and auxin-based tropisms are deliberately designed. They have merits of appropriately balancing exploring & exploiting and self-adaptively varying population size to reduce redundant computation. The effective multi-objective strategies including the fast non-dominated sorting and the farthest-candidate selection are developed for saving and retrieving the Pareto optimal solutions with remarkable approximation as well as uniform spread of Pareto-optimal solutions. With comprehensive evaluation against a suit of benchmark functions, the MORSGO is verified experimentally to be superior or at least comparable to its competitors in terms of the IGD and HV metrics. The MORSGO is then validated to solve the real-world copper strip burdening optimization with different elements. Computation results verifies the potential and effectiveness of the MORSGO to resolve complex industrial process optimization.

In recent years, symbiosis as a rich source of potential engineering applications and computational model has attracted more and more attentions in the adaptive complex systems and evolution computing domains. Inspired by different symbiotic coevolution forms in nature, this paper proposed a series of multi-swarm particle swarm optimizers called PS2Os, which extend the single population particle swarm optimization (PSO) algorithm to interacting multi-swarms model by constructing hierarchical interaction topologies and enhanced dynamical update equations. According to different symbiotic interrelationships, four versions of PS2O are initiated to mimic mutualism, commensalism, predation, and competition mechanism, respectively. In the experiments, with five benchmark problems, the proposed algorithms are proved to have considerable potential for solving complex optimization problems. The coevolutionary dynamics of symbiotic species in each PS2O version are also studied respectively to demonstrate the heterogeneity of different symbiotic interrelationships that effect on the algorithm’s performance. Then PS2O is used for solving the radio frequency identification (RFID) network planning (RNP) problem with a mixture of discrete and continuous variables. Simulation results show that the proposed algorithm outperforms the reference algorithms for planning RFID networks, in terms of optimization accuracy and computation robustness.

This paper proposes a novel artificial bee colony algorithm with dynamic population (ABC-DP), which synergizes the idea of extended life-cycle evolving model to balance the exploration and exploitation tradeoff. The proposed ABC-DP is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. ABC-DP is then used for solving the optimal power flow (OPF) problem in power systems that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results, which are also compared to nondominated sorting genetic algorithm II (NSGAII) and multi-objective ABC (MOABC), are presented to illustrate the effectiveness and robustness of the proposed method.

Over the last few decades, evolutionary algorithms (EAs) have been widely adopted to solve complex optimization problems. However, EAs are powerless to challenge the constrained optimization problems (COPs) because they do not directly act to reduce constraint violations of constrained problems. In this paper, the robustly global optimization advantage of artificial bee colony (ABC) algorithm and the stably minor calculation characteristic of constraint consensus (CC) strategy for COPs are integrated into a novel hybrid heuristic algorithm, named ABCCC. CC strategy is fairly effective to rapidly reduce the constraint violations during the evolutionary search process. The performance of the proposed ABCCC is verified by a set of constrained benchmark problems comparing with two state-of-the-art CC-based EAs, including particle swarm optimization based on CC (PSOCC) and differential evolution based on CC (DECC). Experimental results demonstrate the promising performance of the proposed algorithm, in terms of both optimization quality and convergence speed.

Terrestrial plants have evolved remarkable adaptability that enables them to sense environmental stimuli and use this information as a basis for governing their growth orientation and root system development. In this paper, we explain the foraging behaviors of plant root and develop simulation models based on the principles of adaptation processes that view root growing as optimization. This provides us with new methods for global optimization. Accordingly a novel bioinspired optimizer, namely the root system growth algorithm (RSGA), is proposed, which adopts the root foraging, memory and communication and auxin-regulated mechanism of the root system. Then RSGA is benchmarked against several state-of-the-art reference algorithms on a suit of CEC2014 functions. Experimental results show that RSGA can obtain satisfactory performances on several benchmarks in terms of accuracy, robustness and convergence speed. Moreover, a comprehensive simulation is conducted to investigate the explicit adaptability of root system in RSGA. That is, in order to be able to climb noisy gradients in nutrients in soil, the foraging behaviors of root system are social and cooperative that is analogous to animal foraging behaviors.

Large-scale radio frequency identification (RFID) network planning (RNP) problem has been proven to be a NP-hard issue, which can be formulated as a high-dimensional nonlinear optimization problem with a mixture of discrete and continuous variables and uncertain parameters. First, a two-level optimization model for RFID network planning based on distributed decision making (DDM) is presented in this paper. In this model, the mixed discrete and continuous planning variables, namely the number, location, and radiate power of RFID readers are optimized. In each level of the optimization model, the different objectives to determine optimal values for these planning variables are as follows: (i) minimization of total installation cost of RFID network in the top-level; (ii) maximization of tag coverage and network reliability, and minimization of reader interference in the lower-level. In order to solve the proposed model effectively, this work proposes an efficient approach for RNP problem, namely the hybrid artificial bee colony optimizer (HABC), which employs the natural life-cycle mechanism to cast the original ABC framework to a cooperative and population varying fashion. In the proposed HABC, individuals can dynamically shift their survival states and population size varies dynamically according to the local fitness landscape during the executions of algorithm. These new characteristics of HABC help to avoid redundant search and maintain diversity of population in complex environments. Experiments are conducted on a set of CEC2005 and discrete benchmarks for evaluating the proposed algorithm. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm outperforms the reference algorithms for planning RFID networks, in terms of optimization accuracy and computation robustness.

This paper proposes a novel optimization scheme by hybridizing an artificial bee colony optimizer (HABC) with a bee life-cycle mechanism, for both stationary and dynamic optimization problems. The main innovation of the proposed HABC is to develop a cooperative and population-varying scheme, in which individuals can dynamically shift their states of birth, foraging, death, and reproduction throughout the artificial bee colony life cycle. That is, the bee colony size can be adjusted dynamically according to the local fitness landscape during algorithm execution. This new characteristic of HABC helps to avoid redundant search and maintain diversity of population in complex environments. A comprehensive experimental analysis is implemented that the proposed algorithm is benchmarked against several state-of-the-art bio-inspired algorithms on both stationary and dynamic benchmarks. Then the proposed HABC is applied to the real-world applications including data clustering and image segmentation problems. Statistical analysis of all these tests highlights the significant performance improvement due to the life-cycle mechanism and shows that the proposed HABC outperforms the reference algorithms.

A new bionic algorithm is proposed based on the incentive mechanism of plant root branching, regrowing and tropisms.A new plant root foraging strategy simulates the plant root tropism mechanism and sets up the dynamics mechanism of root growing rapidly towards the global optima.The auxin concentration is set up to determine how to select new growing points and branching number of roots.The mainroot regrowing and the lateral-roots regrowing operators can make appropriate balances between exploration and exploitation. In this contribution, a novel bionic algorithm inspired by plant root foraging behaviors, namely artificial root foraging optimization (ARFO) algorithm, is designed and developed. The incentive mechanism of ARFO is to mimic the adaptation and randomness of plant root foraging behaviors, e.g., branching, regrowing and tropisms. A mathematical architecture is firstly designed to model the plant root foraging pattern. Under this architecture, the effects of the tropism and the self-adaptive growth behaviors are investigated. Afterward, the arithmetic realization of ARFO derived from this framework is presented in detail. In order to demonstrate the optimization performance, the proposed ARFO is benchmarked against several state-of-the-art reference algorithms on a suit of CEC 2013 and CEC 2014 functions. Computational results show a high performance of the proposed ARFO for searching a global optimum on several benchmarks, which indicates that ARFO has potential to deal with complex optimization problems.

For printable electronics fabrication, a major challenge is the print resolution and accuracy delivered by a drop-on-demand piezoelectric inkjet printhead. In order to meet the challenging requirements of printable electronics fabrication, this paper proposes a novel restructured artificial bee colony optimizer called HABC for optimal prediction of the droplet volume and velocity. The main idea of HABC is to develop an adaptive and cooperative scheme by combining life-cycle, Powell’s search and crossover-based social learning strategies for complex optimizations. HABC is a more biologically-realistic model that the reproduce and die dynamically throughout the foraging process and the population size varies as the algorithm runs. With the crossover operator, the information exchange ability of the bees can be enhanced in the early exploration phase while the Powell’s search enables the bees deeply exploit around the promising area, which provides an appropriate balance between exploration and exploitation. The proposed algorithm is benchmarked against other four state-of-the-art bio-inspired algorithms using both classical and CEC2005 test function suites. Then HABC is applied to predict the printing quality using nano-silver ink. Statistical analysis of all these tests highlights the significant performance improvement due to the beneficial combination and shows that the proposed HABC outperforms the reference algorithms.

Most nature-inspired algorithms simulate intelligent behaviors of animals and insects that can move spontaneously and independently. The survival wisdom of plants, as another species of biology, has been neglected to some extent even though they have evolved for a longer period of time. This paper presents a new plant-inspired algorithm which is called root growth optimizer (RGO). RGO simulates the iterative growth behaviors of plant roots to optimize continuous space search. In growing process, main roots and lateral roots, classified by fitness values, implement different strategies. Main roots carry out exploitation tasks by self-similar propagation in relatively nutrient-rich areas, while lateral roots explore other places to seek for better chance. Inhibition mechanism of plant hormones is applied to main roots in case of explosive propagation in some local optimal areas. Once resources in a location are exhausted, roots would shrink away from infertile conditions to preserve their activity. In order to validate optimization effect of the algorithm, twelve benchmark functions, including eight classic functions and four CEC2005 test functions, are tested in the experiments. We compared RGO with other existing evolutionary algorithms including artificial bee colony, particle swarm optimizer, and differential evolution algorithm. The experimental results show that RGO outperforms other algorithms on most benchmark functions.

The artificial bee colony (ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow (OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.

A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on K-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH) is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional), 10 CEC2005 benchmark functions (30-dimensional), and a real-world problem (multilevel image segmentation problems). Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests.

This paper proposes a multi-hive multi-objective bee algorithm (M 2OBA) for optimal power flow (OPF) in power systems. The proposed M2OBA extend original artificial bee colony (ABC) algorithm to multi-objective and cooperative mode by combining external archive, comprehensive learning, greedy selection, crowding distance, and cooperative search strategy. Our algorithm uses the concept of Pareto dominance and comprehensive learning mechanism to determine the flight direction of a bee and maintains nondominated solution vectors in external archive based on greedy selection and crowing distance strategies. With cooperative search approaches, the single population ABC has been extended to interacting multi-hive model by constructing colony-level interaction topology and information exchange strategies. With six mathematical benchmark functions, M2OBA is proved to have significantly better performance than three successful multi-objective optimizers, namely the fast non-dominated sorting genetic algorithm (NSGA-II), the multi-objective particle swarm optimizer (MOPSO), and the multi-objective ABC (MOABC), for solving complex multi-objective optimization problems. M2OBA is then used for solving the real-world OPF problem that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results, which are also compared to NSGA-II, MOPSO, and MOABC, are presented to illustrate the effectiveness and robustness of the proposed method. c 2014 Elsevier Ltd. All rights reserved.

There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell’s pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm intelligence algorithms which inspired by the foraging behavior of honey bee swarms. It has been widely used in numerical and engineering optimization problems. This paper presents a hybrid artificial bee colony (HABC) model to improve the canonical ABC algorithm. The main idea of HABC is to enhance the information exchange between bees by introducing the crossover operator of genetic algorithm to ABC. With suitable crossover operation, valuable information is fully utilized and it is expected that the algorithm can converge faster and more accurate. Eight versions of HABC algorithm combined by different selection and crossover methods under the model were proposed and tested on several benchmark functions. Then, the settings of the new parameter crossover rate for two well performed HABC versions are tested to verify their best values. Finally, four rotated functions and four shifted functions are used to test the performance of the two algorithms on complex functions and asymmetric functions. Experiment results showed that these two versions of HABC algorithm offer significant improvement over the original ABC and are superior to other two state of the art algorithms on some functions.

This paper presents a general optimization model gleaned ideas from root growth behaviours in the soil. The purpose of the study is to investigate a novel biologically inspired methodology for complex system modelling and computation, particularly for optimization of higher-dimensional numerical function. For this study, a mathematical framework and architecture are designed to model root growth patterns of plant. Under this architecture, the interactions between the soil and root growth are investigated. A novel approach called "root growth algorithm" (RGA) is derived in the framework and simulation studies are undertaken to evaluate this algorithm. The simulation results show that the proposed model can reflect the root growth behaviours of plant in the soil and the numerical results also demonstrate RGA is a powerful search and optimization technique for higher-dimensional numerical function optimization.

In this paper, an improved multi-objective ABC algorithm based on k-means clustering, called CMOABC, is proposed. For keeping the population diversity, the multi-swarm technology based on k-means clustering is employed to decompose the population into many clusters. Due to each subcomponent evolving separately, after every specific iterations, the population will be re-clustered to facilitate information exchange among different clusters. CMOABC is applied to solve the real-world Optimal Power Flow (OPF) problem that considers the cost, loss, and emission impacts as the objective functions. The simulation results demonstrate that, compared to NSGA-II, MOPSO, and MOABC, the proposed CMOABC is superior for solving OPF problem, in terms of optimization accuracy.

This paper presents a novel optimization algorithm, namely hierarchical artificial bee colony optimization (HABC), which employs a pool of optimal foraging strategies to extend the classical artificial bee colony framework to cooperative and hierarchical fashion. The higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel. At the same time, the comprehensive learning method with crossover and mutation operator is applied to enhance the global search ability between species. Furthermore, HABC is applied in predicting the droplet characteristic based on lumped element modeling methods. The simulation results demonstrate that the effectiveness of the proposed method.

Bacterial Foraging Optimization (BFO) is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior ofE. colibacteria. Up to now, BFO has been applied successfully to some engineering problems due to its simplicity and ease of implementation. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques. This paper first analyzes how the run-length unit parameter of BFO controls the exploration of the whole search space and the exploitation of the promising areas. Then it presents a variation on the original BFO, called the adaptive bacterial foraging optimization (ABFO), employing the adaptive foraging strategies to improve the performance of the original BFO. This improvement is achieved by enabling the bacterial foraging algorithm to adjust the run-length unit parameter dynamically during algorithm execution in order to balance the exploration/exploitation tradeoff. The experiments compare the performance of two versions of ABFO with the original BFO, the standard particle swarm optimization (PSO) and a real-coded genetic algorithm (GA) on four widely-used benchmark functions. The proposed ABFO shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.

Multiobjective optimization has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on artificial bee colony (ABC) to deal with multi-objective optimization problems. ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. It uses less control parameters, and it can be efficiently used for solving multimodal and multidimensional optimization problems. Our algorithm uses the concept of Pareto dominance to determine the flight direction of a bee, and it maintains nondominated solution vectors which have been found in an external archive. The proposed algorithm is validated using the standard test problems, and simulation results show that the proposed approach is highly competitive and can be considered a viable alternative to solve multi-objective optimization problems.

This paper presents a novel Multi-swarm Particle Swarm Optimizer called PS2O, which is inspired by the coevolution of symbiotic species in natural ecosystems. The main idea of PS2O is to extend the single population PSO to the interacting multi-swarms model by constructing hierarchical interaction topology and enhanced dynamical update equations. With the hierarchical interaction topology, a suitable diversity in the whole population can be maintained. At the same time, the enhanced dynamical update rule significantly speeds up the multi-swarm to converge to the global optimum. The PS2O algorithm, which is conceptually simple and easy to implement, has considerable potential for solving complex optimization problems. With a set of 17 mathematical benchmark functions (including both continuous and discrete cases), PS2O is proved to have significantly better performance than four other successful variants of PSO.

This paper proposed a novel bio-inspired optimizer, namely the root system growth algorithm (RSGA), which adopts the root foraging, memory and communication, and auxin-regulated mechanisms of the root system. When tested against benchmark functions, the RSGA markedly outperforms the CMA-ES, PSO, GA, and DE algorithms in terms of accuracy, robustness and convergence speed.

Robust Principal Component Analysis is generalized to a multi-objective optimization problem, named as Multi-objective Robust Principal Component Analysis (MRPCA) in this paper. We aim to solve MRPCA via Evolutionary Algorithm. To the best knowledge of authors, this is the first attempt to use evolutionary algorithm to solve MRPCA problem, which is a high dimension convex optimization problem. Specifically, one of the popular evolutionary algorithm, NSGA-II, is tested on MRPCA problem. The curse of dimensionality is observed when the dimension of MRPCA problem increases. Since this problem is convex, which is a friendly structure, we propose a modified NSGA-II by introducing a new mutation method: ADMM (Alternating Direction Method of Multipliers) mutation. Numerical experiments show our modified NSGA-II algorithm converges much faster than the standard one.

This chapter presents a novel multilevel threshold approach based on improved particle swarm optimization called as hybrid particle swarm optimization (HPSO), which can combine the advantages of the particle swarm optimization and the neighbor searching of artificial bee colony algorithm for color image segmentation. Experimental results show that the proposed approach adopts the multilevel threshold technique based on the improved HPSO algorithm to obtain a higher quality adequate segmentation; at the same time, it can also reduce the CPU processing time and eliminate the particles falling into local minima.

An improved multiobjective ABC algorithm based onK-means clustering, called CMOABC, is proposed. To fasten the convergence rate of the canonical MOABC, the way of information communication in the employed bees’ phase is modified. For keeping the population diversity, the multiswarm technology based onK-means clustering is employed to decompose the population into many clusters. Due to each subcomponent evolving separately, after every specific iteration, the population will be reclustered to facilitate information exchange among different clusters. Application of the new CMOABC on several multiobjective benchmark functions shows a marked improvement in performance over the fast nondominated sorting genetic algorithm (NSGA-II), the multiobjective particle swarm optimizer (MOPSO), and the multiobjective ABC (MOABC). Finally, the CMOABC is applied to solve the real-world optimal power flow (OPF) problem that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results demonstrate that, compared to NSGA-II, MOPSO, and MOABC, the proposed CMOABC is superior for solving OPF problem, in terms of optimization accuracy.

The development of radio frequency identification (RFID) technology generates the most challenging RFID network planning (RNP) problem, which needs to be solved in order to operate the large-scale RFID network in an optimal fashion. RNP involves many objectives and constraints and has been proven to be a NP-hard multi-objective problem. The application of evolutionary algorithm (EA) and swarm intelligence (SI) for solving multiobjective RNP (MORNP) has gained significant attention in the literature, but these algorithms always transform multiple objectives into a single objective by weighted coefficient approach. In this paper, we use multiobjective EA and SI algorithms to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP, instead of transforming multiobjective functions into a single objective function. The experiment presents an exhaustive comparison of three successful multiobjective EA and SI, namely, the recently developed multiobjective artificial bee colony algorithm (MOABC), the nondominated sorting genetic algorithm II (NSGA-II), and the multiobjective particle swarm optimization (MOPSO), on MORNP instances of different nature, namely, the two-objective and three-objective MORNP. Simulation results show that MOABC proves to be more superior for planning RFID networks than NSGA-II and MOPSO in terms of optimization accuracy and computation robustness.

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC), to tackle complex high-dimensional problems. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operator is applied to enhance the global search ability between species. Experiments are conducted on a set of 20 continuous and discrete benchmark problems. The experimental results demonstrate remarkable performance of the HABC algorithm when compared with other six evolutionary algorithms.

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.

Most bio-inspired algorithms simulate the behaviors of animals. This paper proposes a new plant-inspired algorithm named Root Mass Optimization (RMO). RMO simulates the root growth behavior of plants. Seven well-known benchmark functions are used to validate its optimization effect. We compared RMO with other existing animal-inspired algorithms, including artificial bee colony (ABC) and particle swarm optimization (PSO). The experimental results show that RMO outperforms other algorithms on most benchmark functions. RMO provides a new reference for solving optimization problems.

Bio-inspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biology life cycle, this paper presents a novel optimization algorithm called Lifecycle-based Swarm Optimization. LSO algorithm simulates biologic life cycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection and mutation. Experiments were conducted on 7 unimodal functions. The results demonstrate remarkable performance of the LSO algorithm on those functions when compared to several successful optimization techniques.

This paper proposed an Improved Bacterial Foraging Optimization (IBFO) algorithm to enhance the optimization ability of original Bacterial Foraging Optimization. In the new algorithm, Social cooperation is introduced to guide the bacteria tumbling towards better directions. Meanwhile, adaptive step size is employed in chemotaxis process. The new algorithm is tested on a set of benchmark functions. Canonical BFO, Particle Swarm Optimization and Genetic Algorithm are employed for comparison. Experiment results show that the IBFO algorithm offers significant improvements over the original BFO algorithm and is a competitive optimizer for numerical optimization.

This paper proposes a novel bacterial colony foraging (BCF) algorithm for complex optimization problems. The proposed BCF extend original bacterial foraging algorithm to adaptive and cooperative mode by combining bacterial chemotaxis, cell-to-cell communication, and a self-adaptive foraging strategy. The cell-to-cell communication enables the historical search experience sharing among the bacterial colony that can significantly improve convergence. With the self-adaptive strategy, each bacterium can be characterized by focused and deeper exploitation of the promising regions and wider exploration of other regions of the search space. In the experiments, the proposed algorithm is benchmarked against four state-of-the-art reference algorithms using a set of classical test functions.

This paper presents the study of modelling root growth behaviours in the soil. The purpose of the study is to investigate a novel biologically inspired methodology for optimization of numerical function. A mathematical framework is designed to model root growth patterns. Under this framework, the interactions between the soil and root growth are investigated. A novel approach called “root growth algorithm” (RGA) is derived in the framework and simulation studies are undertaken to evaluate this algorithm. The simulation results show that the proposed model can reflect the root growth behaviours and the numerical results also demonstrate RGA is a powerful search and optimization technique for numerical function optimization.

RFID network planning (RNP) problem is a core challenge in the widespread application of RFID networks. In this paper, we develop a new mathematical model for planning RFID networks with multiple objectives, including carbon emissions, economic efficiency, load balance, interference between readers and coverage. Bacteria Foraging oriented by Particle Swarm Optimization (BF-PSO) is to solve optimize the proposed model. To demonstrate the effectiveness and efficiency of BF-PSO, the simulation results are compared with Bacteria Foraging Optimization (BFO), a real-coded Genetic Algorithm (RGA) and the Self-adaptive ES (SA-ES) on the RFID network planning problem.

We develop an optimization model for risk management in a virtual enterprise environment based on a novel multiswarm particle swarm optimizer called PS2O. The main idea of PS2O is to extend the single population PSO to the interacting multiswarms model by constructing hierarchical interaction topology and enhanced dynamical update equations. With the hierarchical interaction topology, a suitable diversity in the whole population can be maintained. At the same time, the enhanced dynamical update rule significantly speeds up the multiswarm to converge to the global optimum. With five mathematical benchmark functions, PS2O is proved to have considerable potential for solving complex optimization problems. PS2O is then applied to risk management in a virtual enterprise environment. Simulation results demonstrate that the PS2O algorithm is more feasible and efficient than the PSO algorithm in solving this real-world problem.

Multi-objective optimization has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on Artificial Bee Colony (ABC) to deal with multi-objective optimization problems. ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. It uses less control parameters and it can be efficiently used for solving multimodal and multidimensional optimization problems. Our algorithm uses the concept of Pareto dominance to determine the flight direction of a bee and it maintains nondominated solution vectors which have been found in an external archive. The proposed algorithm is validated using the standard test problems ZDT1 to ZDT3 and ZDT6, and simulation results show that the proposed approach is highly competitive and that can be considered a viable alternative to solve multi-objective optimization problems.

Article Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents a variation on the original ABC algorithm, namely the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. In this work, CABC algorithm is used for optimizing six widely-used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO) and its cooperative version (CPSO) have been studied. The simulation results showed that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness and convergence speed.

In order to assist designer in color planning during product development, a novel synthesized evaluation method is presented to evaluate color-combination schemes of multi-working modes products (MMPs). The proposed evaluation method considers color-combination images in different working modes as evaluating attributes, to which the corresponding weights are assigned for synthesized evaluation. Then a mathematical model is developed to search for optimal color-combination schemes of MMP based on the proposed evaluation method and two powerful search techniques known as Evolution Algorithms (EAs) and Swarm Intelligence (SI). In the experiments, we present a comparative study for two EAs, namely, Genetic Algorithm (GA) and Difference Evolution (DE), and one SI algorithm, namely, Particle Swarm Optimization (PSO), on searching for color-combination schemes of MMP problem. All of the algorithms are evaluated against a test scenario, namely, an Arm-type aerial work platform, which has two working modes. The results show that the DE obtains the superior solution than the other two algorithms for color-combination scheme searching problem in terms of optimization accuracy and computation robustness. Simulation results demonstrate that the proposed method is feasible and efficient.

Bacterial Foraging Optimization (BFO) is a novel optimization algorithm based on the social foraging behavior of E. coli bacteria. However, the original BFO algorithm possesses a poor convergence behavior compared to the other successful nature-inspired algorithms. This paper presents a variation on the original BFO algorithm, namely the Cooperative Bacterial Foraging Optimization (CBFO). The cooperative approaches used here resulted in a significant improvement in the performance of the original BFO algorithm in terms of convergence speed, accuracy and robustness. The experiments compare the performance of two variants of CBFO with the original BFO, the standard PSO and a real-coded GA on a set of 4 widely-used benchmark functions. The proposed new method shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.

Bacterial Foraging Optimization (BFO) is a novel optimization algorithm based on the social foraging behavior of E. coli bacteria. However, the original BFO algorithm possesses a poor convergence behavior compared to the other successful nature-inspired algorithms. In order to accelerate the convergence speed of the bacterial colony near global optima, two cooperative approaches have been applied to BFO that resulted in a significant improvement in the performance of the original algorithm in terms of convergence speed, accuracy and robustness. The performance of the proposed cooperative variants are compared to the original BFO, the standard PSO, and a real-coded GA on a set of 4 widely-used benchmark functions, demonstrating their superiority.